Bibliographic record
Abstract
Agradecimentos: This ‘Essays and Perspectives’ article is a product of the ‘Applied Metaecology Workshop’, organized by LS, PIP & MAL in Ilhabela, Brazil, from March 13–17, 2016 and funded by FAPESP (São Paulo Research Foundation; grant 2015/17984-9). We thank Melina Leite and the Graduate Program in Ecology of the University of São Paulo for logistical support. We thank FAPESP (grants 2014/10470-7 to AM, 2013/04585-3 to DL, 2013/50424-1 to TS and 2015/18790-3 to LS), CNPq (Productivity Fellowships 301656/2011-8 to JAFDF, 308205/2014-6 to RP, 306183/2014-5 to PIP and 307689/2014-0 to VDP), the National Science Foundation (DEB 1645137 to JGH), the Natural Sciences and Engineering Council of Canada (SJL, PPN), and the Academy of Finland (grants 257686 and 292765 to MC) for support. This work contributes to the Labex OT-Med (no. ANR-11-LABX-0061), funded by the French government through the A*MIDEX project (no. ANR-11-IDEX-0001-02). We thank Jan Bengtsson for constructive criticism on a previous version of this manuscript
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".