Low host recognition tendency revealed by experimentally induced parasitic egg laying in the common goldeneye (<i>Bucephala clangula</i>)
Bibliographic record
Abstract
Host-parasite relatedness has been suggested to promote the evolution of conspecific brood parasitism, an alternative reproductive tactic pursued by females in several animal taxa. An essential prerequisite for relatedness to promote brood parasitism is accurate kin recognition, including the recognition of related hosts by parasites. I performed a field experiment to address the accuracy of host recognition by parasites in the common goldeneye (Bucephala clangula), a cavity-nesting duck. I studied whether parasites discriminate between experimental nests that did not have a host (i.e., new nest boxes that contained chicken eggs dyed to mimic the colour of common goldeneye eggs) and real nests that did have a host (i.e., active nests that progressed to incubation). Parasitic egg laying in the experimental nests was not constrained by the lack of contemporarily available nests that had a host; it was also not constrained by the lack of suitable and empty nest sites. There was no difference in the start of parasitic laying between the experimental and real nests. The experimental nests and real nests were equally parasitized. The findings suggest that host recognition by parasites is not sophisticated in the common goldeneye, questioning the possible function of accurate kin recognition in brood parasitism in this species.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".