Mobilizing practitioners to support the Emergency Recovery Plan for freshwater biodiversity
Bibliographic record
Abstract
Abstract Freshwater biodiversity loss is one of the greatest environmental threats in our changing world. Although declines have been reported extensively in the literature, much less attention has been devoted to solving the freshwater biodiversity crisis relative to other ecosystems. The recently proposed Emergency Recovery Plan for Freshwater Biodiversity (Tickner et al., 2020, BioScience, 70 (4), 330–342) outlines an ambitious but necessary set of overarching actions that can help “bend the curve” for freshwater biodiversity declines. This plan is timely given the present opportunity to adjust freshwater biodiversity targets in international biodiversity agreements and to encourage meeting targets of relevant Sustainable Development Goals. Yet, relying solely on a trickle down from such agreements to national and local scales will likely take too long, given the immediate urgency of the situation. Here, we advocate for a broader, concerted effort from all actors to ensure the Emergency Recovery Plan meaningfully influences the actions of practitioners at a local scale. We outline the roles and responsibilities of actors involved with policy, research, professional bodies and societies, advocacy, and industry, as well as practitioners themselves, in achieving this goal. It is our hope that this overview facilitates the real‐world actions needed to execute the Emergency Recovery Plan so that we can indeed “bend the curve” for freshwater biodiversity.
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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 | 0.046 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".