Modelling the life‐history variation of Arctic charr
Bibliographic record
Abstract
Abstract – A model based on proximate considerations of life histories of Atlantic salmon,Salmo salar, was examined for its applicability to fit the variation in life‐history of wild Arctic charr,Salvelinus alpinus, based on a qualitative assessment of information related to growth and lipid dynamics of Arctic charr. The original salmon model is discussed in context of modifications required to account for added complexities in the life history of Arctic charr in relation to anadromy versus residency. A study from North Norway shows that individual charr that emigrate from the lakes to the sea, maintain a high growth rate in the lake in late summer and early autumn compared with resident fish. Their relatively low lipid level in autumn combined with a high rate of change of lipid during winter was associated with postponement of maturation in the anadromous individuals. Individuals that remain resident in the lake arrested growth in autumn. Their high lipid level in autumn combined with a low rate of change of lipid during winter was associated with maturation the following summer, without emigration from freshwater. Results from this and other related studies show similarities with the model derived from lipid and growth dynamics of Atlantic salmon. The adjusted charr model illustrates possible proximate explanations for the high variation in life‐history strategies of Arctic charr. However, the model does not account for the characteristic return migration of immature charr into freshwater several weeks after their entry to the sea. The proximate physiological stimulus for this movement of immature fish is not entirely clear.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".