The marine temperature and depth preferences of Arctic charr (<i>Salvelinus alpinus</i>) and sea trout (<i>Salmo trutta</i>), as recorded by data storage tags
Bibliographic record
Abstract
Abstract Eleven Arctic charr ( Salvelinus alpinus ) (370–512 mm) and eight sea trout ( Salmo trutta ) (370–585 mm in length) were tagged externally or internally with depth‐ and temperature‐measuring data‐storage tags (DST) before they were released into the sea in the Alta Fjord in north Norway in June 2002. All sea trout were recaptured after they spent 1–40 days at sea, while all Arctic charr were recaptured after 0.5–33 days at sea. On average, trout preferred water about 0.6 m deeper and 1.3°C warmer than Arctic charr. Arctic charr spent >50% of their time between 0 and 1 m depth, while trout spent >50% of their time between 1 and 2 m depth. Both species spent >90% of their time in water no deeper than 3 m from the water surface. However, sea trout dove more frequently and to greater depths (max. 28 m) than Arctic charr (max. 16 m), and these deep dives were most frequently performed at the end of the sea migration. Arctic charr demonstrated a diel diving pattern, staying on average about 0.5 m deeper between 08:00 hours and about 15:00 hours than during the rest of the 24 h, even though there was continuous daylight during the experiments. When comparing data obtained from the DSTs with temperature measurements within the fjord system, the two species were observed to select different feeding areas during their sea migration, the sea trout choosing the inner and warmer parts of the fjord, in contrast to the Arctic charr that preferred the outer, colder parts of the fjord. The observed differences in migration behaviour between the two species are discussed in relation to species preferences for prey and habitat selection, and their optimal temperatures for growth.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".