Bibliographic record
Abstract
Tables v1.8.0 Diff since v1.7.0 Closed issues: Performance issue with Tables.columns fallback functions at 1st run with Tables.Schema = nothing and Base.IteratorSize = SizeUnknown() (#273) Make Tables.dictrowtable use OrderedDict (#274) Tables.Columns accepts table sources as input rather than AbstractColumns as stated in its docstring (#283) Tables.dictcolumntable produces columns with #undef instead of missing in struct-valued columns (#286) Tables.dictcolumntable results in #undef values (#289) OrderedDict deprecation in DictColumnTable (#294) Support Vector{Dict{String,Any}} as a Table format (#295) Merged pull requests: Remove istable for a value of type AbstractMatrix (#198) (@bkamins) OrderedDict for dictrowtable (#277) (@mathieu17g) Recommend defining materializer(::Type{<:MyType}) (#282) (@nalimilan) add getrows (#284) (@CarloLucibello) Ensure defaultarray with missing types initializes with missing. Fixe… (#288) (@quinnj) Create Invalidations.yml (#290) (@ranocha) Change getrows -> subset (#292) (@quinnj) add ByRow (#293) (@bkamins) Support AbstractDict with String keys as tables (#296) (@quinnj)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.685 | 0.792 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".