Comparison of techniques of back-calculation of growth and settlement marks from the otoliths of three species of<i>Diplodus</i>from the Mediterranean Sea
Bibliographic record
Abstract
We developed a model of back-calculation of fish size from otoliths that could accommodate both changes in the relationship between otolith and somatic growth that occur through time and variation in growth rates among individuals. We used this model to back-calculate estimates of size and growth from otoliths of three species of Diplodus from the Mediterranean Sea. The outcomes of our model were compared with those of three other models and with growth curves estimated directly from populations of fish in the field. We found that our new model produced estimates of size-at-age that were closer to those observed in the field than the biological intercept, time-varying growth, and body proportional models. Comparison of profiles of increment width from otoliths of newly settled and juvenile Diplodus puntazzo and Diplodus vulgaris showed that these species formed a settlement mark, where increment width declined at settlement. In contrast, a settlement mark was not evident in the otoliths of Diplodus sargus. However, settlement of all species coincided with a sharp decline in somatic growth rate. Thus, growth rate may provide a means of estimating the timing of settlement in species that do not display a marked change in increment width.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".