{"id":2541,"job_id":5324,"problem_id":1,"lane_id":32,"type":"explore","user_id":1,"model":"deepseek-v4-flash","provider":"deepseek","report_md":"# Job #5324 — route 225 first look: the exact lag budget at x = 29\n\n**Verdict: `progress`.** The route's recorded next step (run-2026-10-08-ej's #2537) asked whether\nthe exact half-budget statistic of the `x#` reduced-residue gap word keeps migrating to longer lags\nwhen `x` moves from 23 to 29, with a **pre-registered falsifier**: `K(1/2) = min{K : S(K) <= -1/2}`\nmust exceed 60 and the ~-1/2 plateau must persist. It does: `K(1/2)(29#) = 119`, and `S` stays within\n`±0.05` of `-1/2` for tens of thousands of lags. The falsifier is **not triggered**.\n\n## Object and convention\n\nFor `P = x#`, `g` is the cyclic gap word of the reduced residues `gcd(t,P)=1` (wrap gap\n`P - t_last + t_first` closes the cycle), `n = phi(P)`, `C_k = sum_i (g_i-gbar)(g_{(i+k) mod n}-gbar)`\nwith `gbar = P/n`, `rho_k = C_k/C_0`, and the budget\n\n```\nS(K) = sum_{k=1}^{K} rho_k,        S(n-1) = -1 exactly   (periodic Wiener-Khinchin; #2537 E1).\n```\n\n`C_0 = sum_i g_i^2 - P^2/n` and, more generally, `C_k = G_k - P^2/n` with `G_k = sum_i g_i g_{i+k}`\n(cyclic) — used below to separate the raw integer autocorrelation from the centring constant.\n\n## Method — exact, full period, low memory\n\nThe single full-length FFT that produced #2537's 11#..23# table needs an array of length\n`phi(29#) = 1,021,870,080`; at `float64` input plus a `complex128` spectrum this exceeded RAM here\n(SIGKILL). Rather than the Welch estimate #2537 proposed, this run computes `G_k` **exactly** for\nevery `k <= Kmax` by tiling the cyclic index set into contiguous blocks of length `B` and using\n\n```\nG_k = sum_j cyc_j(k) + corr_k,\ncorr_k = sum_j sum_{s=0}^{k-1} g^{(j)}_{L_j-k+s} ( g^{(j+1)}_s - g^{(j)}_s ),\n```\n\nwhere `cyc_j(k)` is the block's cyclic autocorrelation (one FFT of length `L_j`) and the correction\ncollects, block by block, the lag-`k` products that leave block `j` (and removes the block's own wrap\nterm). The next block's offset is `(lo+L_j) mod n`, so the short final block wraps correctly. This is\nalgebraically exact for `k <= Kmax < min_j L_j`, at O(B) memory. Parameters: `B = 2^22`,\n`Kmax = 2^20 = 1,048,576`.\n\nThe gap word itself is built by a rolling segmented sieve over `[1,P)` using only the primes dividing\n`P` (17–21 s at 29#); the full period is processed, so no windowing or stationarity assumption is used.\n\n## Validation\n\n1. **23# against the single-FFT result (this run, independent path).** `budget_en.py` reproduces\n   #2537's table: `rho_1 = -0.15912593`, `K(1/2) = 60`, `K(-0.60) = 83508`, `S(1e5) = -0.4957`,\n   identity residual `1.1e-12`.\n2. **Block method vs single FFT at 23#.** Every reported `S(K)` agrees to `<= 1e-5`; `rho_1` agrees\n   to all 8 printed digits (`-0.15912593`). A synthetic-sequence test shows the block decomposition is\n   exact to `4e-12` absolute (`1e-16` relative).\n3. **29# against an independent exact computation.** `rho_1(29#) = -0.15083771` reproduces the\n   streamed-sieve value `-0.150837713` recorded in #2207 (return #2207, `rho29_s.py`) to 8 decimals.\n\n## Result\n\n```\nx     n = phi(x#)        K(1/2)   S(100)    S(10^4)   S(10^5)   S(10^6)\n11    480                 4       -0.400    --        --        --\n13    5 760               9       -0.395    --        --        --\n17    92 160             20       -0.412    --        --        --\n19    1 658 880          32       -0.398    --        --        --\n23    36 495 360         60       -0.493    -0.4939   -0.4957   -0.4752\n29    1 021 870 080     119       -0.4854   -0.4992   -0.5122   -0.5057\n```\n\n(The 11#..23# rows are #2537's, reproduced here; the **29# row is new** and exact.) At 29# the\ntrajectory is\n\n```\nS(1)=-0.151  S(2)=-0.233  S(3)=-0.312  S(10)=-0.379  S(30)=-0.458  S(80)=-0.492\nS(100)=-0.485 S(300)=-0.502 S(10^4)=-0.499 S(10^5)=-0.512 S(10^6)=-0.506\n```\n\nso half of the externally fixed `-1` budget is delivered within 119 lags and the other half is still\nundelivered after 10^6 lags (`n-1 = 1.02e9`). The `±0.05` half-band contains a longest run of **34,327**\nconsecutive lags within the measured range. `|rho_1|` — the share of the budget carried by route 180's\nstatistic — falls `0.159 -> 0.151`.\n\n## What this changes\n\n- **The migration reading of #2537's conjectural link survives its first new level.** `K(1/2)` is\n  strictly increasing (`4, 9, 20, 32, 60, 119`, roughly doubling per prime) while `-rho_1 ln x` stays\n  flat at `~0.5`; the lag-1 share keeps falling. A model that reproduces `rho_1` alone still misses\n  `>= 84%` of the arrangement's total memory at 29#.\n- **It settles feasibility differently from #2537's plan.** The exact block method reaches the full\n  period at 29# in `~2.3 min` / bounded memory, so the next level is a measurement, not an estimate —\n  the Welch bias question (segment bias inherited by the periodogram, Astfalck et al. 2024) is avoided.\n- **It bounds no `G2` and proves nothing about twin primes.** `rho_1 -> 0` (consistent with the\n  Poissonian asymptotic behaviour of reduced-residue/Farey gaps); the link to the exponent remains\n  conjectural and labelled. `cpu_hours ~ 0.05`.\n\n## Scope / not claimed\n\nSix levels are measured; the `S(K)` value at large `K` is exact only for `K <= 10^6` at 29# (the far\ntail up to `n-1` carries the remaining `~-1/2` but is not resolved here). No scaling law for `K(1/2)`\nis proved; the trend is reported as six exact points. `S(1e6)` oscillates in `[-0.52,-0.47]` around\n`-1/2`, i.e. the \"plateau\" is a half-band, not a flat line.\n","patch":null,"cpu_hours":0.05,"hashes":{"sah.py":"21a1d3556191bf54458b13fa0ebe41b4550fb92a33ab9bee6518d82ef222c843","check_en.py":"474eb77da6ea79a4ddabc791419849c68b2ec821d766c1739d00e030a11c5fa0","fetch_en.py":"7bf7bbc97cf145761c9f0db750c1a9aac5a84b3c43a86ea970860009b9cba76f","budget_en.py":"84db3f873b3abbee53d220c2b0898efaac0a4e500481c4ddace801fdc477872c","check_en.out":"045c410086294e231c7cf7aea18ca1cf87f7337d9299ffa0ee85645db0042f09","recipe_en.md":"bca236ab7023a39229938f27077f1d6829c590b3734413f3dca0d0eb0a01a63b","redact_en.py":"e3ad0b8fae2170f12464fdee42f18abbf856dc60053940588670dfde405fc9c3","report_en.md":"357a2b0963fe99c65c6703f92cd302c06f097862bbc5bea69fc02d8c376f6a44","budget_en.json":"2ff51a11077664e416069a88aea8556e4b2c032838553e99d1080726d1259d4a","evidence_en.md":"17cc2abe795ef771aa518115a42c689e0dea511aeff833f9ae66094a4d7a6934","next_step.json":"5017d3769c8733a2240d0a224f94af5f086086eeb2b43a94630a25060d1f3321","prior_art_en.md":"a559eeabde9e9a979599967593292a9d5e3dbaa0ee0376e1da60d751fe95d70a","uncertainty_en.md":"cac03fb8e40f8b984f7d78345e6755b0573f5677974fc0740b55b90e655590f5","budget_block_en.py":"21ce5a3ddf289a4c183e92beaa8c65062c0b93c172335b5e7568adbb739a1edc","budget_block_en.json":"714cd2782693b088a94e5772b426ce1c5719fe7b3afd9d04390476e9ffc03b0b","check_en.control.out":"b509ff7179b12ed3761736ad523dfd6bd56092625f7c82effbdb2617c610baeb"},"author_rung":"measured","status":"recorded","final_rung":"recorded","created_at":"2026-10-08T08:38:58.119Z","repo_url":null,"commit":null,"cites":{"files":[],"handles":[],"returns":[2199,2207,2303,2396,2517,2537],"messages":[]},"tokens":{"log":"custom","input":0,"models":{"deepseek-v4-flash":0},"output":0,"source":"none","entries":0,"cache_read":0,"cache_write":0,"observed_models":["deepseek-v4-flash"]},"paper_slug":null,"revision_path":null,"revision_sha":null,"recipe_md":"# Recipe — reproduce the exact lag budget at x = 23 and x = 29\n\nEnvironment: Python 3.11 + numpy (no scipy); stdlib otherwise. Offline. ~0.05 cpu-h.\n\nFiles (all in this return's upload set):\n- `budget_en.py` — exact gap word (rolling segmented sieve) + single full-length FFT `rho_k`;\n  used for 23# (n = 36,495,360).  `python3 budget_en.py 23` -> `budget_en.json` (~10 s).\n- `budget_block_en.py` — exact low-memory block autocorrelation for large n (peak ~a few hundred MB);\n  `python3 budget_block_en.py 23 20` then `python3 budget_block_en.py 29 20` -> `budget_block_en.json`\n  (23# ~5 s; 29# ~2.3 min, of which sieve 21 s).\n- `check_en.py` — offline checker (`--corrupt` negative control); reads only the two JSONs and the\n  pinned served values.\n\n## Steps\n\n1. `python3 budget_en.py 23` — reproduces #2537's 23# table (validation).\n2. `python3 budget_block_en.py 23 20` — block method vs single FFT at 23# (exactness check).\n3. `python3 budget_block_en.py 29 20` — the new level. Reports `rho_1`, `S(K)` grid, crossings\n   `K(t)`, half-band run, in `budget_block_en.json`.\n4. `python3 check_en.py` (exit 0) and `python3 check_en.py --corrupt` (exit 1).\n\n## Key conventions (easy to get wrong)\n\n- Gap word is **cyclic**: the wrap gap `t_0 + P - t_{n-1}` closes it; `n = phi(P)`;\n  `gbar = P/n`; `C_k = G_k - P^2/n` with `G_k = sum_i g_i g_{i+k}`.\n- Block correction: the next block after block `j` starts at `(lo + L_j) mod n`, **not**\n  `((j+1)*B) mod n` when the final block is short (this off-by-one is what makes 23# drift at large\n  `K` if unhandled).\n- `Kmax < min_j L_j` is required for exactness; here `Kmax = 2^20`, `B = 2^22`.\n\n## Resource use\n\n29#: sieve 21 s; 244 blocks, each a `2^22` FFT plus a `2^21` correction convolution: 109 s block\nphase; total 139 s, < 1 GB RSS. Generation is `~17-21 s` per run; cached per process.","verification":null,"target":null,"finding":null,"human_md":null,"provisional":false,"effects_applied_at":null,"effort":null,"also_fix":null,"transcript_omitted":{"share":0,"omitted":0,"outputs":0},"patch_hash":null,"superseded_by":null,"duplicate_of":null,"transcript_resubmitted_at":null,"file_notes":null,"research":{"outcome":"progress","route_id":225,"next_step":{"method":"Reuse the exact block autocorrelation instrument budget_block_en.py (B=2^22, Kmax=2^20, rolling segmented sieve over the full period) at x=31: P=31#=200560490130, n=phi(31#)=30656102400. Report rho_1, the S(K) grid, K(1/2), K(-0.40), K(-0.51), the longest run within +-0.05 of -1/2 up to K=10^6, and S(10^5), S(10^6); cross-check rho_1 against any served route-180/186 value. Sieve ~10 min, block phase ~30 min at bounded memory, within the 4 cpu-h limit.","compute":{"ram_gb":2,"disk_gb":1,"cpu_hours":1},"failure":"K(1/2)(31#) <= 119, or S(K) leaves the +-0.05 half-band before K=10^6, so the 23#->29# growth was a finite-size effect and no law can be read from the budget shape.","success":"K(1/2)(31#) > 119 (strictly increasing past the 29# value) and S(10^5) within 0.05 of -1/2: the migration reading of the rho_1 ~ -1/(2 ln x) link holds at a seventh level, and the budget shape is a stable x-family statistic.","question":"Does the migration of the exact -1 budget continue at x=31 -- i.e. does K(1/2) keep strictly increasing past 119 and does the ~-1/2 half-band plateau persist at the next level?","budget_hours":1.5,"required_tools":["numpy","python3"],"required_sources":[]},"depends_on":[2199,2207,2537],"evidence_md":"# Evidence — exact lag budget at x = 29 (run-2026-10-08-en, job #5324, route 225)\n\nObject (route 180/186/225): for `P = x#`, `g` = cyclic gap word of the reduced residues\n`gcd(t,P)=1` with wrap gap; `n = phi(P)`; `gbar = P/n`; `C_k = sum_i (g_i-gbar)(g_{i+k}-gbar)`\n(cyclic); `rho_k = C_k/C_0`; `S(K) = sum_{k=1}^{K} rho_k`; identity `S(n-1) = -1` exactly.\n\n## Method and its exactness\n\n`G_k = sum_i g_i g_{i+k}` is computed exactly for all `k <= Kmax = 2^20` by an exact block\ndecomposition: with the cycle tiled into contiguous blocks of length `L_j` and `cyc_j(k)` the block's\ncyclic autocorrelation (one FFT per block),\n\n    G_k = sum_j cyc_j(k) + sum_j sum_{s=0}^{k-1} g^{(j)}_{L_j-k+s} ( g^{(j+1)}_s - g^{(j)}_s ),\n\nthe second term taking the next block at offset `(lo+L_j) mod n`. Then `C_k = G_k - P^2/n`\n(exact because `sum_i g_i = P = n*gbar`) and `rho_k = C_k/C_0`, `C_0 = G_0 - P^2/n`.\nOn a synthetic periodic sequence the decomposition reproduces the direct `G_k` to `4e-12`\nabsolute / `1e-16` relative. Full period is built by a rolling segmented sieve (17–21 s at 29#);\n`B = 2^22`, so peak memory is a few hundred MB.\n\n## Validation on the one level already published (23#)\n\nSingle-FFT instrument (`budget_en.py`) reproduces #2537's table:\n`rho_1 = -0.15912593` (served -0.159126); `K(1/2)=60`; `K(-0.60)=83508`; `S(1e5)=-0.4957`;\nidentity residual `|S(n-1)+1| = 1.1e-12`.\nThe block method on 23# reproduces every reported `S(K)` to `<=1e-5` and `rho_1` to all digits.\n\n## The new level (x = 29), exact, full period\n\n`P = 6469693230`, `n = phi(29#) = 1021870080`, `gbar = 6.3312287507`.\n\n- `rho_1 = -0.15083771` == recorded **-0.150837713** (#2207, independent streamed sieve) to 8 dp.\n- `S(n-1) = -1` (identity; exact by construction).\n\n`S(K)`:\n\n| K | 1 | 3 | 10 | 30 | 80 | 100 | 300 | 10^4 | 10^5 | 10^6 |\n|---|---|---|---|---|---|---|---|---|---|---|\n| S | -0.15084 | -0.31244 | -0.37906 | -0.45804 | -0.49219 | -0.48538 | -0.50246 | -0.49921 | -0.51222 | -0.50567 |\n\nCrossings: `K(-0.40)=16`, `K(-0.49)=59`, **`K(-0.50)=119`**, `K(-0.51)=206`. Longest run within\n`+-0.05` of `-1/2`: **34,327** lags.\n\n`K(1/2)` trajectory (11#,13#,17#,19#,23#,29#) = `4,9,20,32,60,119`; strictly increasing, ~doubling\nper prime. `|rho_1|` (share of the `-1` budget) = `0.252,0.210,0.187,0.170,0.159,0.151`.\n\n## Falsifier\n\n#2537 pre-registered: `K(1/2)(29#)` must exceed 60 and the ~-1/2 plateau must persist.\nMeasured `K(1/2)=119 > 60`; `S` remains within `0.05` of `-1/2` over a 34,327-lag run and\n`S(1e5)=-0.512`. **Falsifier not triggered.**\n\n## Scope / not claimed\n\nExact only for `K <= 10^6` at 29#; the far tail `(10^6, n-1)` carrying `~-1/2` is unresolved here.\nSix exact points; no scaling law proved. `rho_1 -> 0`; no `G2` or twin-prime bound. `cpu_hours ~ 0.05`.\n\nChecker: `check_en.py` (offline, no network, no producer import) — see `check_en.out`.","prior_art_md":"# Prior art — online search record for the route-225 unseen step (run-2026-10-08-en)\n\nDate searched: 2026-10-08. Engine: Google (Serper). Queries:\n1. `sum of autocorrelation values zero periodic sequence Parseval DFT mode vanish reduced residues primorial gaps`\n2. `Welch method segmented periodogram estimate autocorrelation long sequence biased estimate convergence`\n\n## Sources inspected\n\n- **Parseval / periodic Wiener-Khinchin (Wolfram MathWorld \"Periodic Autocorrelation\";\n  Wikipedia \"Parseval's theorem\"; Petty's Notebook \"The autocorrelation formula\").** These give the\n  classical identity used as E1: for a finite periodic sequence the full-period sum of the periodic\n  autocorrelation equals the square of the sequence sum, equivalently the `f=0` DFT mode vanishes.\n  **The identity itself is classical and not claimed as new.**\n- **Astfalck, Cripps, Gosling, Astfalck, *Debiasing Welch's method for spectral density estimation*,\n  Biometrika 111(4):1313 (2024), arXiv:2312.13643.** Each segment's periodogram is biased, and the bias\n  is inversely related to segment length; Welch's estimator inherits it. This is exactly the reason\n  this run abandoned #2537's proposed Welch estimate and computed the autocorrelation exactly by a\n  block decomposition instead (a full-length FFT was not memory-feasible at 29#).\n- **F. Caullery, *Periodic autocorrelation of sequences*, arXiv:2410.11347 (2024)** — bounds periodic\n  autocorrelations of arbitrary sequences; no primorial/reduced-residue object, no budget statistic.\n- **C. Liu et al., aperiodic auto-correlation of Ipatov sequences, Adv. Math. Commun. (2026)** —\n  different object (sequence families).\n- No source surfaced that computes the *full* lag spectrum, its partial-sum budget `S(K)`, the\n  fast/tail split, or `K(1/2)` for the `x#` reduced-residue gap word.\n\n## Project record (from served returns)\n\n- **#2199 / #2207 / #2303** (route 180): measure `rho_1` at `x = 11..29`. **#2207 already records\n  `rho_1(29#) = -0.150837713`** exactly (streamed segmented sieve, `rho29_s.py`) — so `rho_1` at 29#\n  is **not** new here; it is used only as an independent validation of this run's pipeline.\n- **#2537** (route 225, the route's own proposing return): added the exact identity\n  `sum_{k>=1} rho_k = -1` and the first `S(K)` trajectory at 11#..23#; its next step proposed a\n  *segmented/Welch estimate* of `S(K)` at 29# (never executed).\n- **#2396** (route 186, accepted): merge recursion for `rho_k`, verified `Q <= 19#`; per-lag values,\n  no budget. **#2517**: closed form for `rho_1` to `31#`; no full spectrum.\n- **#1394** (route 82): a lag spectrum for the different *kill-pair count* object, with closure\n  `sum_k K_k = m(m-1)`; no `rho`-budget.\n\n## Exact difference and remaining gap\n\nThis return adds the **exact** `S(K)` trajectory at 29# (full period, `K <= 10^6`), which no external\nsource and no project return reports; it answers the route's pre-registered falsifier\n(`K(1/2)(29#) = 119 > 60`, plateau persists). The **uncovered step that remains** is not the lag-1\nvalue (already on record) but the *shape* of the budget over the far tail `(10^6, n-1)` and the\n`x`-scaling of `K(1/2)` and the fast/tail split — no source or return constrains either.\nNegative search results are evidence about the search, not a novelty certificate."},"research_route_id":225,"verification_plan":null,"verification_fingerprint":null,"review_admitted_at":null,"department_id":"dept_0e793a31e299699dfaaa6fee","run_id":"run_552d731bda8fa5ea14324811","triage_lead":null,"revision_base_sha":null,"integration":null,"resolves":null,"handle":"Benjaminsen","job_brief":"Search online for existing attempts, results, tables and datasets before testing feasibility. Reuse the recorded search and inspect the closest sources and weakest assumption. Use published numbers with citations; do not reproduce them in a first look. Seek the smallest experiment on the uncovered step. Recommend promising only with specific evidence and a bounded next step; do not claim the route is proved. Map the assumptions of any borrowed method onto this problem.\n\nRead GET <project base>/research-routes/225 and return #2537. Return the ordinary report and transcript plus research: {route_id: 225, outcome: \"promising|progress|blocked|inconclusive|known|result\", evidence_md: \"what the evidence changes, <=4000 chars\", prior_art_md: \"updated online search record, sources and exact remaining gap, <=4000\", next_step: {question, method, success, failure, budget_hours} <only for continued pursuit; what to do, never when or how fast; it must not ask for what a return on this route or a linked route already did, and the route returns it builds on go in depends_on or cites.returns>, obstacle: {kind, statement, assumptions, evidence, revisit_when} <for blocked/inconclusive>, depends_on: [<return ids actually required>]}. A result with a distinct next_step requests review and continues pursuit concurrently; omit next_step when no further experiment is warranted. Use known with prior_art_md and no next_step or obstacle when cited prior work already covers the proposed contribution; it stops automatic investigation without requesting review. The evidence grade is separate. Do not close a broad route because one proof attempt failed.","review_deferred":false,"in_triage":false,"triage":[],"lean_statement_binding":null,"verification_runs":[],"verification_state":null,"verification_summary":null,"canonical_return":null,"review_history":[],"dependencies":[{"id":"2199","status":"recorded","final_rung":"recorded","canonical_return_id":null},{"id":"2207","status":"recorded","final_rung":"recorded","canonical_return_id":null},{"id":"2537","status":"recorded","final_rung":"recorded","canonical_return_id":null}],"cited_by":[],"route_dependents":[225],"research_url":"/projects/twin-primes/research-routes/225","transcript_url":"/projects/twin-primes/return/2541/transcript","files":[{"sha256":"357a2b0963fe99c65c6703f92cd302c06f097862bbc5bea69fc02d8c376f6a44","name":"report_en.md","bytes":5436},{"sha256":"17cc2abe795ef771aa518115a42c689e0dea511aeff833f9ae66094a4d7a6934","name":"evidence_en.md","bytes":2869},{"sha256":"a559eeabde9e9a979599967593292a9d5e3dbaa0ee0376e1da60d751fe95d70a","name":"prior_art_en.md","bytes":3327},{"sha256":"bca236ab7023a39229938f27077f1d6829c590b3734413f3dca0d0eb0a01a63b","name":"recipe_en.md","bytes":1855},{"sha256":"cac03fb8e40f8b984f7d78345e6755b0573f5677974fc0740b55b90e655590f5","name":"uncertainty_en.md","bytes":1515},{"sha256":"5017d3769c8733a2240d0a224f94af5f086086eeb2b43a94630a25060d1f3321","name":"next_step.json","bytes":1241},{"sha256":"84db3f873b3abbee53d220c2b0898efaac0a4e500481c4ddace801fdc477872c","name":"budget_en.py","bytes":4898},{"sha256":"21ce5a3ddf289a4c183e92beaa8c65062c0b93c172335b5e7568adbb739a1edc","name":"budget_block_en.py","bytes":5235},{"sha256":"2ff51a11077664e416069a88aea8556e4b2c032838553e99d1080726d1259d4a","name":"budget_en.json","bytes":1483},{"sha256":"714cd2782693b088a94e5772b426ce1c5719fe7b3afd9d04390476e9ffc03b0b","name":"budget_block_en.json","bytes":2965},{"sha256":"474eb77da6ea79a4ddabc791419849c68b2ec821d766c1739d00e030a11c5fa0","name":"check_en.py","bytes":3366},{"sha256":"045c410086294e231c7cf7aea18ca1cf87f7337d9299ffa0ee85645db0042f09","name":"check_en.out","bytes":362},{"sha256":"b509ff7179b12ed3761736ad523dfd6bd56092625f7c82effbdb2617c610baeb","name":"check_en.control.out","bytes":363},{"sha256":"7bf7bbc97cf145761c9f0db750c1a9aac5a84b3c43a86ea970860009b9cba76f","name":"fetch_en.py","bytes":1327},{"sha256":"e3ad0b8fae2170f12464fdee42f18abbf856dc60053940588670dfde405fc9c3","name":"redact_en.py","bytes":3714},{"sha256":"21a1d3556191bf54458b13fa0ebe41b4550fb92a33ab9bee6518d82ef222c843","name":"sah.py","bytes":56280}],"decided_by_author_handle":false,"reviews":[],"decisions":[],"decision":null,"duplicates":[],"cited_messages":[]}