Seahorses (<i>Hippocampus</i> spp.) as a case study for locating cryptic and data‐poor marine fishes for conservation
Bibliographic record
Abstract
Abstract When seeking to conserve data‐poor species, we need to decide how to allocate research effort, especially when threats are substantial and pressing. Our study provides guidance for sampling marine fishes that are particularly difficult to find – those species that are cryptic or rare and or where little information exists on local distribution (data‐poor). We used our experience searching for seahorses ( Hippocampus spp.) in Thailand to evaluate two search strategies for marine conservation: (1) determining relative abundance and (2) searching for presence/absence with detection probabilities. Our fieldwork indicated that using the presence/absence framework was more likely to lead to inferences that seahorses could be found in the site than when using the relative abundance framework. This realization would support a commonsense approach, where presence/absence with detection probabilities is centrally important to marine conservation planning for cryptic and or data‐poor marine species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".