The <scp>RACE</scp> for freshwater biodiversity: Essential actions to create the social context for meaningful conservation
Bibliographic record
Abstract
Abstract Freshwater habitats are experiencing two to three times the rate of biodiversity loss of terrestrial and marine habitats. As status quo actions within the conservation community are not reversing the downward trajectory for freshwater biodiversity, we propose four actions to shift the narrative such that freshwater biodiversity is no longer invisible and overlooked, but rather explicitly recognized, valued, and protected: (1) Reshape our relationship with freshwater habitats and biodiversity, (2) Appreciate indigenous knowledge systems relating to freshwater habitats, (3) Connect science more directly to action, and (4) Elevate freshwater habitats as a unique “domain” that requires explicit recognition in conservation planning (RACE). We highlight roles that both freshwater scientists and the wider conservation community can play in implementing the four actions such that the “RACE” can be won.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".