Temporal variability in marine feeding of sympatric Arctic charr and sea trout
Bibliographic record
Abstract
The marine feeding pattern of anadromous brown trout (sea trout)Salmo truttaand Arctic charrSalvelinus alpinuswas studied during June to August in 1992–1993 and 2000–2004 in a fjord in northern Norway. In general sea trout fed proportionally more on fishes than on crustaceans and insects (81, 1 and 18% by mass, respectively) by comparison with Arctic charr (52, 25 and 22% by mass, respectively). HerringClupea harengusdominated the total fish diet of both species, but the Arctic charr also fed significantly on gadoids and sandlanceAmmodytesspp. While sea trout became virtually all piscivorous at fork lengths (LF) ≥250 mm, the Arctic charr was ≥400 mmLFbefore shifting totally to a fish diet. Despite annual variation in diet and forage ratios, there was a clear shift in diet from 1992–1993 to 2000–2004. Sandlance and different crustaceans constituted most of the diet during the initial period with a shift towards gadoids and especially herring during the latter period. This shift seemed to be associated with a high abundance of herring larvae during the latter sampling period, indicating a preferential selection on herring when present, particularly by sea trout. Furthermore, an index indicated dietary overlap in years with intensive feeding on herring of both species, and usually differences in the trophic ecology during years feeding mostly on other prey species. In combination, it was hypothesized that the two species reflect the type of marine prey present within a fjord system over time, and therefore provide an index of variation in the production and biological diversity of their potential prey within fjords.
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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 | 0.000 | 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".