Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Matlab users behold! Your time of easyness has finally come! After all these years of being left behind by us, you finally can enjoy ezc3d without the struggle to install ezc3d by yourself, as the binaries are now provided! Friends, enjoy! What's Changed Added an example how to modifiy a file in Python by @pariterre in https://github.com/pyomeca/ezc3d/pull/324 Fixed regex for python setup install by @pariterre in https://github.com/pyomeca/ezc3d/pull/329 Fixed subframes inversion in rotation for python by @pariterre in https://github.com/pyomeca/ezc3d/pull/330 Added support for arm64 on macos for the matlab binding by @pariterre in https://github.com/pyomeca/ezc3d/pull/333 Push matlab binaries github action by @pariterre in https://github.com/pyomeca/ezc3d/pull/335 Removed useless multiple matlab compilation by @pariterre in https://github.com/pyomeca/ezc3d/pull/336 Video for Matlab binaries by @Ipuch in https://github.com/pyomeca/ezc3d/pull/337 Typo by @pariterre in https://github.com/pyomeca/ezc3d/pull/338 Full Changelog: https://github.com/pyomeca/ezc3d/compare/Release_1.5.10...Release_1.5.11
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.119 |
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 it