Bibliographic record
Abstract
As milestone years go, 2023 is a big one for Journal of Experimental Biology.Celebrating its 100th anniversary, the journal is looking forward to the future, and the arrival of Matthew McHenry as a new Monitoring Editor is a fabulous way to begin the year's events.'JEB is likely the first scientific journal that I read', says McHenry, adding that he was thrilled and honoured when JEB Editor-in-Chief Craig Franklin invited him to join the team of Editors.Growing up in Pennsylvania, USA, McHenry remembers that his parents emphasised the importance of education and a series of inspiring biology teachers set him on course to study Biology at Vassar College, USA.There, he was introduced to the field of biomechanics by John Long, Jr. 'John had such a profound influence on me over those 4 years', says McHenry, crediting him with kindling the passion for biomechanics that still inspires him 30 years on.'I entered college not even being aware that professors conducted research and left with a desire to become an academic scientist.I was swept up in the excitement of collaborative research well before I was formulating novel questions of my own', he says.After graduating from Vassar, McHenry joined Mimi Koehl at University of California,
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.288 | 0.183 |
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".