Morphometric and genetic analyses of two sympatric morphs of Arctic char (<i>Salvelinus alpinus</i>) in the Canadian High Arctic
Bibliographic record
Abstract
Multivariate morphometric analyses were used to examine variation in head, body, and fin shape between two sympatric morphotypes of Arctic char ( Salvelinus alpinus (L., 1758)) from Lake Hazen, Ellesmere Island, Nunavut, Canada. Population structure of the Lake Hazen Arctic char was examined using five microsatellite loci. The “small” morph was found to have a larger (primarily deeper) head, larger and more elongate fins, and a deeper lateral profile than the “large” morph. The morphs also differed in allometric growth patterns. The large and small morphs do not appear to represent genetically distinct populations. The head morphology of the Lake Hazen small and large morphs exhibited similarities to benthic and pelagic morphs (respectively) from other lakes. We hypothesize that the large morph may be adapted to high-efficiency swimming and that the small morph may be adapted to low-efficiency, high-acceleration swimming. Such functional trade-offs are not uncommon among fish specializing in dispersed or mobile prey (fish and plankton) and benthic prey, respectively. The lack of apparent genetic differentiation between the morphs may suggest that the morphological differences result, to some extent, from phenotypic plasticity. Based on these results and previous analyses, it seems reasonable to conclude that Lake Hazen Arctic char represent a resource polymorphism.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".