Geometric morphometric analysis of fish scales for identifying genera, species, and local populations within the Mugilidae
Bibliographic record
Abstract
Geometric morphometric methods (GMMs) were used to determine if scale morphology can discriminate between genera, species, geographic variants, and stocks of mullet (Mugilidae). GMMs were used because they allow standard multivariate analyses while preserving information about scale shape, which is important in making biological interpretations of results. The method was tested on ctenoid scales from mullets collected from different areas of the Gulf of Mexico and Aegean Sea. Scales were submitted to generalised procrustes analysis, followed by principal components analysis of resulting shape coordinates. Principal component scores were submitted to cross-validated discriminant analysis to determine the efficacy of scale landmarks in discriminating by taxon and population. Fish scale form was least effective in discriminating populations from nearby areas, better when populations are more geographically dispersed, and best between species and genera. Scale form variations reflected previous genetic studies that differentiated congeneric Mugil cephalus and Mugil curema, which are distinct from other Mugilidae. The method is nondestructive, quick, and less costly than genetic analysis, thus allowing many individuals to be screened.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".