GUIDELINE MATERIALS AND DOCUMENTATION FOR THE GENETIC DIVERSITY INDICATORS OF THE MONITORING FRAMEWORK FOR THE KUNMING-MONTREAL GLOBAL BIODIVERSITY FRAMEWORK
Bibliographic record
Abstract
sponsorship: COMPETING INTERESTS The authors have declared that no competing inter- ests exist. ACKNOWLEDGMENTS These materials are based on the co-creation ex- perience of the first pilot multinational assessment of the genetic diversity indicators, and on interactions with practitioners, researchers and students of sev- eral institutions across the world. We are particular- ly grateful to the Swedish Environmental Protection Agency, the Ad Hoc Technical Expert Group on In- dicators for the Kunming-Montreal Global Biodi- versity Framework, and to the following people for providing feedback and ideas: Akio Takenaka, Ale- jandra Dominguez lvarez, Alexander Llanes-Que- vedo, Alice Hughes, Ana Wegier, Ashley Hamilton, Atsaves Angelica, Austin Koontz, Bastian Silva, Belma Kalamujie Stroil, Caitlin Miller, Catherine E Grueber, W Chris Funk, Emma Suzuki Spence, Er- ica Robertson, Eugenia Zarza, Fleur Visser, Gaelle Brahy, Georgina Wood, Glenn M Shea, Henrik Thurfjell, Hesiquio Benitez, Irene Ramos, Iris Lang, Isa-Rita Russo, Juan Francisco Ornelas, Katie Mil-lette, Keiichi Fukaya, Kira Cullmann, Libertad Arre- dondo-Amezcua, Lily Durkee, Lucia Ruiz, Luke Dedecke, Malte Julius Benedikt Lehmann, Malte Lehmann, Margaret E. Hunter, Maria Alejandra Ro- driguez-Morales, Maria Camila Latorre, Marlien van
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".