Paradox of otolith shape indices: routine but overestimated use
Bibliographic record
Abstract
The identification of fish species using otolith shape has been common in many fields of the marine science. Different analytical processes can be applied for the morphological discrimination, but reviewing the literature we have found conceptual and statistical limitations in the use of shape indices and wavelets (contour analysis), being specially worrying in the first case due to their widespread routine use. In the present study, 42 species were classified using otolith shape indices and wavelets and applying traditional and machine learning classifiers and performance measures (accuracy, Cohen’s kappa statistic, sensitivity, and precision). Our results were conclusive; wavelets were a more adequate option for the classification of species than shape indices, independently of classifiers and performance measures considered. The artificial neural network and support vector machine provided the highest values for all performance measures using wavelets. In all cases, the measures of sensitivity and precision pointed out a higher confusion between some otolith patterns using shape indices. Therefore, we strongly discourage the routine use of shape indices for the identification of species.
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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 | 0.057 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".