Growth Autocorrelation in Atlantic Bluefin Tuna <scp> <i>Thunnus thynnus</i> </scp> Larvae in the Northwest Mediterranean and the Gulf of Mexico
Bibliographic record
Abstract
ABSTRACT Growth and survival rates during the early life stages are key vital parameters driving population dynamics of fish. Growth rates generally present a pattern of autocorrelation. Growth autocorrelation is stronger when faster and slower growing individuals continue to grow faster and slower. Thus, the extent of growth autocorrelation can be a tool for considering potential effects of early growth rates on subsequent growth rates in the life history of fish. In the present study, we applied a group‐level growth autocorrelation analysis to Atlantic bluefin tuna (Thunnus thynnus ) larvae in the northwest Mediterranean (MED) and the Gulf of Mexico (GOM). Based on the otolith increment width data compiled mainly from published datasets, the pattern of growth autocorrelation was described for the species and compared between the MED and GOM populations. Atlantic bluefin tuna showed the highest levels of growth autocorrelation during the early life stages compared with various fish species. Their characteristics supported the general hypothesis that the species and populations with higher growth rates have stronger growth autocorrelation, extending the hypothesis to cover large pelagic piscivorous fish. Therefore, the maternal effects and the environmental variability that larvae encounter right after hatching would be even more critical in survival dynamics and useful for predicting the recruitment dynamics than previously recognized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".