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
28. We thank A. Hamilton and members of the Ecker laboratory for critical reading of the manuscript and stimulating discussions. J.M.A. was in part supported by a postdoctoral fellowship from the Spanish Min-isterio de Educación y Ciencia. A.N.S. was a recipient of a predoctoral fellowship from the Department of Energy (ER20162-A0003). The W.L.C. laboratory was supported by a grant from the Canadian National Research Council Vice-President’s fund. P. G. was on a sabbatical recess from Cinvestav-Irapuato, Mexico, and was supported in part by a fellowship from CONACYT, Mexico. We thank K. Davis and colleagues at Paradigm Genetics for their assistance in bulking up low-seed T1 lines. This work was supported by an NSF Arabidopsis 2010 project grant (MCB O115103)
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it