Laboratory testing of a growth hypothesis for juvenile squid <i>Loligo pealeii</i> (Cephalopoda: Loliginidae)
Bibliographic record
Abstract
Growth modeling in squid has been hampered by a paucity of raw growth data on live individuals. We reared wild juvenile squid Loligo pealeii, for up to 97 days post capture, to determine the form of growth and to test the hypothesis that a 5°C difference in temperature would significantly affect growth rates. Precapture growth rates (the instantaneous relative growth rate or percent increase in body mass per day (IRGR)) of 811% were estimated using statolith age data. Laboratory growth rates over a maximum of 97 experimental days fell into two phases in which most L. pealeii grew exponentially, albeit at a slower rate in phase 2. In both phases, the values of IRGR were significantly higher for L. pealeii reared at 20°C than for those reared at 15°C, being respectively, 4.36 and 2.69 in phase 1 and 2.57 and 1.63 in phase 2. This study provides strong evidence of phase-specific temperature sensitivity in squid growth. The IRGR values obtained were used to simulate the growth of squid hatched in nature from May to September in a simple predictive model. The growth simulations indicated that, by the end of phase-1 growth, squid hatched in June and July were two and three times the weight, respectively, at the same age, as squid hatched in May, owing to their exposure to warmer temperatures.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".