Quantifying food competition between two demersal fish species from spatiotemporal stomach content data
Bibliographic record
Abstract
Inference on competition is often made on indirect patterns of potential competition, such as population trends and spatiotemporal overlap in diet and distribution. However, these indicators do not test if the contested resources are limited in supply, nor if they decline as competitor biomass increases. Using stomach content and biomass data, we evaluate food competition between Atlantic cod ( Gadus morhua) and flounder ( Platichthys spp.) in the Baltic Sea. We quantify diet overlap and fit geostatistical mixed models to evaluate effects of local-scale covariates on stomach contents. The dietary overlap is low and does not decline with predator density. We find that cod feed less on the isopod Saduria entomon at high flounder densities. However, the total prey weight in cod is not affected by flounder densities. This suggests that interspecific food competition is not limiting the overall feeding of cod but affects its diet composition. In addition, we find support for intraspecific food competition in flounder and large cod. Our study illustrates the importance of local-scale processes when inferring competition from stomach content data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".