Structure et fonction des écosystèmes par l'imagerie : la relation entre hétérogénéité et biodiversité
Bibliographic record
Abstract
Surround yourself with people who make you happy.People who make you laugh , who help you when you 're in need.People who genuinely care.They re the ones worth keeping in your life.Everyone else is just passing through -Karl Marx l would like to acknowledge the many people to whom l learned from , helped achieve this work and most of all made it enjoyable.l thank my parents, orm and Diane, for supporting my meanderings and helping me follow my interests unbounded.l thank my sister Dena -an artist with the curiosity of a scientist -for the distractions when distractions were needed.Satsuko for her unwavering support, motivation and for thinking all my ideas are great ... even when they're not mine.l give many thanks to Mario Liebergessell for the numerous occasions
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".