Bibliographic record
Abstract
What's Changed now actually download the new models by @alinelena in https://github.com/ACEsuit/mace/pull/721 Evaluate test sets separately for different heads by @ThomasWarford in https://github.com/ACEsuit/mace/pull/681 Add pre-processing config file option by @ElliottKasoar in https://github.com/ACEsuit/mace/pull/664 Add mace_mp medium performance benchmark by @hatemhelal in https://github.com/ACEsuit/mace/pull/647 change learning rate for multihead ft by @ilyes319 in https://github.com/ACEsuit/mace/pull/727 add option to rescale number of ft sample by @ilyes319 in https://github.com/ACEsuit/mace/pull/736 Clean up unused Polynomial Cutoff Class from ZBLBasis, remove r_max argument. by @CompRhys in https://github.com/ACEsuit/mace/pull/569 allow custom cache based on XDG_CACHE_HOME env variable, addresses #724 by @alinelena in https://github.com/ACEsuit/mace/pull/755 Fix default outpout_file in select_head, and add argument to list heads by @bernstei in https://github.com/ACEsuit/mace/pull/772 change default mp model to mpa model+bump version by @ilyes319 in https://github.com/ACEsuit/mace/pull/758 solve jit backward compatibility by @ilyes319 in https://github.com/ACEsuit/mace/pull/778 fix the reshape irreps for jit backward by @ilyes319 in https://github.com/ACEsuit/mace/pull/779 fix formatting by @ilyes319 in https://github.com/ACEsuit/mace/pull/780 Develop by @ilyes319 in https://github.com/ACEsuit/mace/pull/781 Develop by @ilyes319 in https://github.com/ACEsuit/mace/pull/785 make cueq optional dep and add special test by @ilyes319 in https://github.com/ACEsuit/mace/pull/786 Full Changelog: https://github.com/ACEsuit/mace/compare/v0.3.9...v0.3.10
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.326 | 0.442 |
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".