Upholding science-based risk assessment under a weakened <i>Endangered Species Act</i>
Bibliographic record
Abstract
Since the United States enacted its first species-at-risk legislation in 1966, many jurisdictions have similarly adopted legislation aimed at conserving biodiversity through the identification of species at risk of extinction, the protection of these species from harm, and the establishment of recovery programs (Ray and Ginsberg 1999; Waples et al. 2013). Although these statutes have successfully thwarted extinction for hundreds of species, they have also failed to recover many at-risk species (Schwartz 2008; Mooers et al. 2010; Evans et al. 2016). As global extinction rates approach those observed during the five mass extinction events in Earth’s history (Barnosky et al. 2011), robust, well-implemented conservation laws are critically needed to slow the loss of biodiversity (Westwood et al. 2019; Leclère et al. 2020).
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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 | 0.042 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".