Fish length, diet, and depth drive <i>Anisakis</i> levels in a zooplankton-feeding fish
Bibliographic record
Abstract
Parasites play a key but overlooked role in the functioning of marine systems. Understanding the drivers of parasite infection in fish is thus crucial. Anisakis levels in the Atlantic horse mackerel Trachurus trachurus resulted from both biotic and environmental drivers. Ontogenic variation of the diet was the first driver of Anisakis level. The nonlinear pattern confirmed that the variation in parasitism was related to ontogenetic dietary shifts. The impact of depth on diet also explained the effect of depth on parasites. In deeper areas, fish only access pelagic prey, where Anisakis presence is high, consistent with the pelagic nature of the parasite. Finally, relative proportions of parasitic burden by tissue appeared to be driven by lipid content. In lipid-rich species like T. trachurus, Anisakis can find favorable conditions in the body cavity and in muscle and avoid the liver. Parasitism levels in fish are thus the result of patterns at play at different levels, and these factors should be better considered before being able to include parasites in an integrated vision of marine ecosystem functioning.
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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 | 0.000 | 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) | 0.003 | 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".