Modeling trophic interactions between parental common murres and capelin off the northeast Newfoundland coast
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.This presentation focuses on trophic interactions between capelin (Mallotus villosus) and its primary avian predator, the common murre (Uria aalge) on Funk Island during the breeding season. Diet is evaluated through parental deliveries to the chicks and prey availability is estimated from pelagic trawl data within the murre's foraging range. Diet composition is assessed using percentage by number (%N), with its confidence limits obtained by bootstrapping. Since the common murre is a capelin specialist and feeds on capelin larger than 100mm (suitable capelin), three prey categories were considered: small capelin (100-140mm total length), large capelin (>140mm total length) and others (prey species other than capelin). Considering the densities of these prey groups as explanatory variables and assuming a multinomial probability distribution for the individual prey deliveries, the common murre's diet is being modeled in two different ways. One is purely statistical and uses a standard multicategory logit model. The second one, derived from ecological theory, estimates the probabilities of consuming different prey categories from a generalized form of the multispecies Holling functional response. Both models describe well the observed diets, but the model with ecological roots has a better fit than the purely statistical one. Furthermore, in years when the abundance of suitable capelin was high the proportions of large and small capelin consumed were not significantly different, while they were in years of low suitable capelin abundance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".