10 Must Dos from Biodiversity Science 2022
Bibliographic record
Abstract
The authors of the 10 Must Knows from Biodiversity Science (2022, DOI: 10.5281/zenodo.6257527, 10MustKnows) have developed their scientific findings further into 10 Must Dos from Biodiversity Science (10MustDos). The 10MustDos correspond with ten concrete recommendations for political actions that can be implemented in the short term. They are intended to serve as a guide for negotiations at the 15th UN Biodiversity Conference (CBD COP 15, 7-19 December 2022 in Montréal). In addition, they also aim at supporting practical policy-making in Germany, Europe and worldwide through well-founded scientific knowledge with the overarching goal to protect global biodiversity and to stop the man-made extinction of species. The proposed solutions open up possibilities for action which are in alignment with the goals of the UN Decade for the Restoration of Ecosystems (2021-2030) and contribute to the 17 Sustainable Development Goals (SDGs) which are to be implemented by all states by 2030 in order to tackle the biodiversity, climate, and equity crisis collectively.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.370 | 0.239 |
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".