10 Must-Dos aus der Biodiversitätsforschung 2022
Bibliographic record
Abstract
Die Autorinnen und Autoren der 10 Must-Knows aus der Biodiversitätsforschung (2022, 10.5281/zenodo.6257476, 10MustKnows) haben ihre wissenschaftlichen Erkenntnisse zu 10 Must-Dos aus der Biodiversitätsforschung (10MustDos) weiterentwickelt. Die 10MustDos entsprechen zehn konkreten und kurzfristig umsetzbaren Handlungsempfehlungen für die Politik. Sie sollen als Wegweiser für die 15. Weltnaturkonferenz (CBD COP 15, 7.-19. Dezember 2022 in Montréal) fungieren. Zudem sollen sie auch in der praktischen Politikgestaltung in Deutschland, Europa und weltweit durch fundierte wissenschaftliche Erkenntnisse helfen, die globale Biodiversität zu schützen und das menschengemachte Artensterben zu stoppen. Die vorgeschlagenen Lösungswege eröffnen Handlungsmöglichkeiten, die im Einklang mit den Zielen der UN-Dekade zur Wiederherstellung von Ökosystemen stehen und in die bis 2030 von allen Nationalstaaten umzusetzenden 17 Nachhaltigkeitsziele (SDGs) einzahlen, um die Biodiversitäts-, Klima- und Gerechtigkeitskrise zu bewältigen.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.211 | 0.117 |
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".