Gillnet selectivity and size and age structure of an alpine Arctic char (<i>Salvelinus alpinus</i>) population
Bibliographic record
Abstract
The aim of the present study was to address possible implications of biased sampling for the commonly adopted uni- and bi-modal size structures and unimodal age structures in Arctic char (Salvelinus alpinus) populations. Multimesh gill nets were used to sample an allopatric population of Arctic char in an alpine lake in central Norway. Direct estimates of gillnet selectivity for different length-classes of Arctic char were obtained by mark-recapture experiments and by successive removal methods. The observed size and age structure in gillnet samples was significantly different from the estimated size and age structure of the Arctic char population when catches were corrected for gillnet selectivity. An observed unimodal size and age structure was a direct result of gillnet selectivity, as smaller and younger fish were underrepresented in gillnet catches. Moreover, an abrupt increase in gillnet selectivity for large Arctic char was related to a niche shift to cannibalism. A model that explains bimodal size distributions in gillnet catches as a result of ontogenetic behavioural change is presented. Complex ontogenetic growth and mortality patterns that have been suggested to be essential in structuring modal Arctic char populations were superfluous in explaining the observed modal population structure in the present study.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".