Assessing species diversity of Coral Triangle artisanal fisheries: A DNA barcode reference library for the shore fishes retailed at Ambon harbor (Indonesia)
Bibliographic record
Abstract
The Coral Triangle (CT), a region spanning across Indonesia and Philippines, is home to about 4,350 marine fish species and is among the world's most emblematic regions in terms of conservation. Threatened by overfishing and oceans warming, the CT fisheries have faced drastic declines over the last decades. Usually monitored through a biomass-based approach, fisheries trends have rarely been characterized at the species level due to the high number of taxa involved and the difficulty to accurately and routinely identify individuals to the species level. Biomass, however, is a poor proxy of species richness, and automated methods of species identification are required to move beyond biomass-based approaches. Recent meta-analyses have demonstrated that species richness peaks at intermediary levels of biomass. Consequently, preserving biomass is not equal to preserving biodiversity. We present the results of a survey to estimate the shore fish diversity retailed at the harbor of Ambon Island, an island located at the center of the CT that display exceptionally high biomass despite high levels of threat, while building a DNA barcode reference library of CT shore fishes targeted by artisanal fisheries. We sampled 1,187 specimens and successfully barcoded 696 of the 760 selected specimens that represent 202 species. Our results show that DNA barcodes were effective in capturing species boundaries for 96% of the species examined, which opens new perspectives for the routine monitoring of the CT fisheries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".