Using cumulative human‐impact models to reveal global threat patterns for seahorses
Bibliographic record
Abstract
Understanding threats acting on marine organisms and their conservation status is vital but challenging given a paucity of data. We studied the cumulative human impact (CHI) on and conservation status of seahorses (Hippocampus spp.)-a genus of rare and data-poor marine fishes. With expert knowledge and relevant spatial data sets, we built linear-additive models to assess and map the CHI of 12 anthropogenic stressors on 42 seahorse species. We examined the utility of indices of estimated impact (impact of each stressor and CHI) in predicting conservation status for species with random forest (RF) models. The CHI values for threatened species were significantly higher than those for nonthreatened species (category based on International Union for Conservation of Nature Red List). We derived high-accuracy RF models (87% and 96%) that predicted that 5 of the 17 data-deficient species were threatened. Demersal fishing practices with high bycatch and pollution were the best predictors of threat category. Major threat epicenters were in China, Southeast Asia, and Europe. Our results and maps of CHI may help guide global seahorse conservation and indicate that modeling and mapping human impacts can reveal threat patterns and conservation status for data-poor species. We found that for exploring threat patterns of focal species, species-level CHI models are better than existing ecosystem-level CHI models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".