Influence du parasitisme des moules sur l'alimentation des limicoles : exemple de l'Huîtrier pie hivernant dans l'Exe
Bibliographic record
Abstract
Parasite loads vary among feeding sites in European oystercatchers, Haematopus ostralegus, hibernating in the Exe Estuary, in Great Britain. This variation might be the result of active or passive selection of non-parasitised mussels (Mytilus edulis) because they are the preferred food of the oystercatcher in the area and are also the intermediate hosts of one of the main parasites of the bird. Parasite loads (Renicola sp. and Psilostomum brevicolle) of the mussels were studied in relation to morphological parameters known to influence the choice of prey by the oystercatcher: length of mussel, meat content, and thickness of shell. Smaller mussels generally carry the lightest parasite loads, but the number of metacercariae of the two parasites potentially consumed in a day varies with feeding site and mussel size. Thus, an oystercatcher will ingest fewer metacercariae of Renicola sp. and P. brevicolle by selecting smaller mussels from bank 1, which is one of the least visited sites in the estuary. However, the birds will have to select mussels of 37 mm or more on banks 30 and 31, which are among the most used sites. We suggest that there is no active selection of non-parasitised mussels, but rather a kind of passive selection as the mussels chosen for their energetic value happen to be also the least parasitised.[Journal translation]
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".