Bimodal size distributions in Arctic char, <i>Salvelinus alpinus</i>: artefacts of biased sampling
Bibliographic record
Abstract
Bimodal population size and age distributions in Arctic char (Salvelinus alpinus (L.)) and hypotheses on growth patterns generating bimodality have drawn considerable attention during the last decade. However, such bimodality has also been suggested to be an artefact of biased sampling. We examined published data sets reporting bimodal size distributions in gill-net samples of Arctic char in order to confront hypotheses on growth patterns generating bimodal population size distributions. Growth patterns were derived from published length-at-age data. Simulations revealed that the observed growth patterns evidently could not generate a bimodal population size distribution. The basic reason for this was that growth did not stagnate strongly enough in the largest size classes of Arctic char. The reliability of growth approximations from length-at-age data was supported by empirical data on back-calculated growth trajectories. Furthermore, differences in year-class strength cannot explain all of the observed bimodal size and age distributions in gill-net samples, as they have been reported to persist over time. Thus, bias in the sampling procedure, which overestimates the frequency of old and large fish, is retained as the only plausible explanation for stable bimodal size distributions often observed in Arctic char gill-net samples.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".