Assessing the food web impacts of an anadromous<scp>A</scp>rctic charr introduction to a sub‐<scp>A</scp>rctic watershed using stable isotopes
Bibliographic record
Abstract
Abstract AnadromousArctic charr,Salvelinus alpinus(L.), was introduced to a sub‐Arctic river–lake system near the village ofKujjuuaq,Nunavik, and the stable isotope values and diets of key resident fish species were used to assess changes in feeding patterns. Stable isotope values for most species did not differ significantly between the pre‐ and post‐introduction periods, with observed shifts being within the bounds of expected natural variation. Lake chub,Couesius plumbeus(Agassiz), were the single species to show a difference between study periods, with a small but significant increase in δ15N. No significant post‐introduction changes were seen in lake trout,Salvelinus namaycush(Walbaum), omnivory or in any of the assessed quantitative food web metrics. Gut contents of major fish species similarly showed significant temporal overlap between the pre‐ and post‐introduction periods, and there was no significant change in species' weight–length relationships. The minor ecological impact was interpreted in relation to the availability of open niches exploitable by ecological generalists such asArctic charr. The explanation accords with the known habitat and feeding flexibility ofArctic charr and the ecological immaturity of sub‐Arctic lakes known to have driven adaptive variation amongArctic charr. Findings suggest that anadromousArctic charr may be introduced at moderate densities to other sub‐Arctic watersheds without major negative food web consequences for other resident fish species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".