Migration patterns, ambient temperature, and growth of Icelandic cod (<i>Gadus morhua</i>): evidence from storage tag data
Bibliographic record
Abstract
Sixty data storage tags were analysed with respect to depth and ambient temperature and for relationships between depth, ambient temperature, and growth. The fish were tagged and released in April 1996–1999 on the spawning grounds at the southwest coast of Iceland. Profiles of depth and ambient temperature suggest that cod (Gadus morhua L.) that spawn in the study area select between two alternatives in foraging strategies, i.e., deep- or shallow-water migrations. The shallow-water fish appear to follow the seasonal trend in temperature characteristic for the shelf waters. The deep-water fish, however, migrate to deeper and cooler waters outside the spawning season and increase their vertical movement. A significant positive relationship between depth and ambient temperature was observed for the shallow-water fish, and a significant negative relationship was observed for the deep-water fish. Daily variation in depth and ambient temperature showed increasing trends with increase in depth, in particular for deep-water fish. Growth was negatively related to depth and positively related to ambient temperature. This indicates higher growth rate of cod that forage in shallow waters versus deeper waters. Supply of food, for which depth may be a reasonable proxy, offers a more likely explanation for this growth pattern than environmental temperature.
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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) | 0.000 | 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 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".