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Record W6893823943 · doi:10.5281/zenodo.3726237

TuringLang/Turing.jl: v0.9.1

2020· other· en· W6893823943 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsPolytechnique MontréalUniversity of British Columbia
Fundersnot available
KeywordsCompilerCode (set theory)sortBenchmark (surveying)Set (abstract data type)Scripting languageTuringSearch engine indexing

Abstract

fetched live from OpenAlex

Turing v0.9.1 Diff since v0.9.0 Closed issues: Juno Progress Metres (#799) Build an offical docker for Turing (#1113) strange sort of parameter names and values in calling describe with 'sections' (#1132) Failure working with MH sampler and MvNormal (#1133) Question: Use of the term multivariate regression in linear regression tutorial (#1143) Tidy up benchmarking scripts (#1144) Question: WARNING: could not import MCMCChains.AbstractChains into Turing ERROR: LoadError: LoadError: UndefVarError: AbstractChains not defined (#1150) Libtask error: setindex! not defined for TArray{Float64,1} (#1155) Missing method invlink in Bijectors 0.6 (#1158) Add compiler doc to navigation (#1159) side navigation bar not working on team page - https://turing.ml/dev/team/ (#1161) Broken link on https://turing.ml/dev/docs/using-turing/dynamichmc (#1171) Merged pull requests: Zygote AD backend (#783) (@mohamed82008) Dockerfile for the Turing (latest, 0.8.3) (#1120) (@KDr2) Fix links to websites (#1130) (@devmotion) Use concrete types for fields of ADVI (#1131) (@devmotion) Use ProgressLogging instead of ProgressMeter (#1134) (@devmotion) Fix MH indexing bug (#1135) (@cpfiffer) Update dockerfile for new release (#1138) (@KDr2) Use Nanosoldier.jl to run benchmarks (#1142) (@KDr2) Use EllipticalSliceSampling (#1145) (@devmotion) Tidy up benchmark files (#1146) (@KDr2) Update my info on website (#1147) (@mohamed82008) Provide default progress loggers (#1149) (@devmotion) Move default model evaluation code to DynamicPPL (#1151) (@phipsgabler) Update members.yml (#1152) (@phipsgabler) CompatHelper: bump compat for "Bijectors" to "0.6" (#1153) (@github-actions[bot]) Disable progress logs in tests (#1156) (@devmotion) Add compiler doc to website (#1160) (@cpfiffer) Move doc files to src/for-developers (#1162) (@cpfiffer) Fixed LinkedIn links (#1163) (@cpfiffer) Save transitions of samplers in Gibbs sampling (#1166) (@devmotion) Fix MH sampler for arrays of distributions (#1167) (@devmotion) FIX: bijector currently returns inverse of what it claims (#1168) (@torfjelde) Do not store transitions in Gibbs sampler (#1169) (@devmotion) ReverseDiff support (#1170) (@mohamed82008) Correct Gibbs link (#1174) (@cpfiffer) Update HISTORY.md (#1177) (@cpfiffer)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.586
Threshold uncertainty score0.591

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0080.009
Open science0.0060.008
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.5860.750

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.239
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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