Fatty acid signatures and stomach contents of four sympatric<scp>L</scp>ake<scp>T</scp>rout: assessment of trophic patterns among morphotypes in<scp>G</scp>reat<scp>B</scp>ear<scp>L</scp>ake
Bibliographic record
Abstract
Abstract Sympatric diversification inLakeTrout is generally linked to differences in habitat use (especially depth) as a result of foraging on different prey items. However, extensive sympatric divergence has taken place in the shallow waters (≤30 m) ofGreatBearLake, with multipleLakeTrout morphs varying in head and fin characteristics. To investigate diet partitioning as a potential explanatory mechanism for this diversification, we assessed trophic characteristics and relationships among four sympatric shallow‐water morphs ofLakeTrout via analyses of fatty acids and stomach contents. Fatty acids and stomach contents both identifiedLakeTrout,Cisco andMysisas key prey items inLakeTrout diets. Interestingly, terrestrial invertebrates were also seasonally important among morphs, reflecting temporal variability of available prey in this arctic lake. Some diet partitioning was observed among morphs;Morph 1 was characterised as a generalist,Morph 3 was more benthic‐oriented, andMorphs 2 and 4 were mainly pelagic feeders. Of the latter,Morph 4 was the most specialised, whereasMorph 2 exhibited alternative feeding tactics of benthic cannibalistic and pelagic piscivorous feeding. Our findings demonstrate that complementary dietary methods can elucidate habits of opportunistic feeders, a task that can often be problematic, given their complex and variable diets. Our results add new information and perspectives on the current model ofLakeTrout differentiation, demonstrating niche partitioning based on benthic versus pelagic habitat use and generalist versus specialist feeding tactics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".