Temperature–growth patterns of individually tagged anadromous<scp>A</scp>rctic charr<i><scp>S</scp>alvelinus alpinus</i>in<scp>U</scp>ngava and<scp>L</scp>abrador,<scp>C</scp>anada
Bibliographic record
Abstract
Abstract Individual measurements of annual, or within‐season growth were determined from tag‐recaptured A rctic charr and examined in relation to summer sea surface temperatures and within‐season capture timing in the U ngava and L abrador regions of Eastern C anada. Differences between two years of growth (2010–2011) were significant for U ngava B ay A rctic charr, with growth being higher in the warmer year. Growth of L abrador A rctic charr did not vary significantly among years (1982–1985). Regional comparisons demonstrated that U ngava A rctic charr had significantly higher annual growth rates and experienced warmer temperatures than L abrador A rctic charr. The higher annual growth of U ngava B ay A rctic charr was attributed to the high sea surface temperatures experienced in 2010–2011 and the localised differences in nearshore productivity as compared to L abrador. Within‐season growth rates of L abrador A rctic charr peaked in J une, declined towards A ugust and were negatively correlated with the length of time spent at sea and mean experienced sea surface temperatures. A quadratic model relating growth rate to temperature best explained the pattern of within‐season growth. Collectively, results suggest that increases in water temperature may have profound consequences for Arctic charr growth in the C anadian sub‐ A rctic, depending on the responses of local marine productivity to those same temperature increases.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".