Identification of the maternal source of young‐of‐the‐year Arctic charr in Lake Hazen, Canada
Bibliographic record
Abstract
Summary A discriminant function analysis model based on carbon and nitrogen stable isotope values was used to identify offspring of piscivorous large‐form and non‐piscivorous small‐form Arctic charr, Salvelinus alpinus, morphotypes from Lake Hazen, Nunavut, Canada. The ability to distinguish between morphotypes in Lake Hazen was based mainly on the separation of δ15N signatures because of adult occupation of significantly different trophic levels. A smaller difference between adult morphotype δ13C values, most likely combined with a potential increase in variation among individuals at the egg stage, a faster turnover rate in carbon relative to nitrogen and the size at which fish can be sampled in the open‐water season in Lake Hazen, probably contributes to the limited use of δ13C in distinguishing between morphotype offspring. Based on young‐of‐the‐year (YOY) origin estimates, the adult morphotypes were estimated to contribute approximately equally to the YOY population; however, the morphotype offspring were differentially distributed among sampled nursery sites. Unequal distribution corresponds with prerequisites suggested for the evolution of trophic specialists within a single population that experiences assortative mating based on trophic specialisation. Differential use of spawning areas and the more or less equal importance of both Lake Hazen forms found in this study, taken alongside previously noted morphological variation and available genetic evidence, suggest that Lake Hazen may be an example of early divergence relative to other Arctic charr populations described in the literature.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".