Bibliographic record
Abstract
Incorporating significant changes by @dsevero: Adaptive codecs Signatures of push and pop are extended to allow for a *context variable. push: (ans_state, symbol, *context) -> (ans_state, *context) pop: (ans_state, *context) -> (ans_state, symbol, *context) Note that, since context is passed via unpacking (i.e. *context), then it is essentially optional. Therefore, previous codecs are still compliant with this new signature. However, the return of push will be at least (ans_state,), so function calls have been adapted accordingly. This means that instead of message = codec.push(message, symbol) we now have message, = codec.push(message, symbol) Multiset codec The multiset codec from https://github.com/facebookresearch/multiset-compression was ported to craystack/codecs/multiset.py. Extra example An extra example, showing how to use the multiset codec for BMNIST with BB-ANS, was added to craystack/examples.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.313 | 0.338 |
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".