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 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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.180 | 0.053 |
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".