<scp>DNA</scp> barcodes identify marine fishes of <scp>S</scp>ão <scp>P</scp>aulo <scp>S</scp>tate, <scp>B</scp>razil
Bibliographic record
Abstract
Anthropogenic impacts are an increasing threat to the diversity of fishes, especially in areas around large urban centres, and many effective conservation actions depend on accurate species identification. Considering the utility of DNA barcoding as a global system for species identification and discovery, this study aims to assemble a DNA barcode reference sequence library for marine fishes from the coastal region of São Paulo State, Brazil. The standard 652 bp 'barcode' fragment of the cytochrome c oxidase subunit I (COI) gene was PCR amplified and bidirectionally sequenced from 678 individuals belonging to 135 species. A neighbour-joining analysis revealed that this approach can unambiguously discriminate 97% of the species surveyed. Most species exhibited low intraspecific genetic distances (0.31%), about 43-fold less than the distance among species within a genus. Four species showed higher intraspecific divergences ranging from 2.2% to 7.6%, suggesting overlooked diversity. Notably, just one species-pair exhibited barcode divergences of <1%. This library is a first step to better know the molecular diversity of marine fish species from São Paulo, providing a basis for further studies of this fauna - extending the ability to identify these species from all life stages and even fragmentary remains, setting the stage for a better understanding of interactions among species, calibrating the estimations about species composition and richness in an ecosystem, and providing tools for authenticating bioproducts and monitoring illegal species exploitation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".