Classification of otoliths of fishes common in the Santa Barbara Basin based on morphology and chemical composition
Bibliographic record
Abstract
Morphological and chemical features of fish otoliths are used to distinguish between populations and stocks. We hypothesized that these features can also be used to distinguish between fishes of different taxonomic groups common in and near the Santa Barbara Basin, including mesopelagic, pelagic, and demersal fish. Sagittal otoliths obtained from 905 fish representing six taxonomic groups were imaged, and 12 geometric and 59 elliptic Fourier morphometric features were extracted. A subset of 143 otoliths was also analyzed for Li, Na, Mg, K, Mn, Sr, and Ba. We used chemical composition in addition to morphology because the latter may be altered between otolith formation and analysis. Two sets of classifiers were made: one using only morphometric features and one using both morphometric and element features. Random forest analysis was generally superior to discriminant function analysis. Highest classification success, evaluated using cross-validation and otoliths of masked identity, was achieved with multiple feature types. The ten strongest discriminatory features of all available feature types were used in the final classification models. Our method is applicable to the classification of otoliths recovered from guts, feces, middens, and sediments as well to classify other biological objects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".