Wetland type differentially affects ectoparasitic mites and their damselfly hosts
Bibliographic record
Abstract
The effect of artificial habitat in altered landscapes on species interactions and their suite of enemies is largely unknown. Water mites have been associated with reduced fitness of model damselflies. Mite parasitism was variable, but higher for Ischnura verticalis damselflies from natural, than from artificial, wetlands in the same region. There were no differences in timing of sampling, temperature during sampling, or host age or sex composition of samples between wetland types. Landscape structure might constrain mite presence or abundance at wetland sites or wetland type might be a better predictor of mites, based on factors such as prey abundance. Fewer mites on damselflies from numerous artificial wetlands means that the strength of parasite‐mediated selection is likely less than would be inferred if only natural wetlands were surveyed. Such effects of human changes in habitats on host species probably occur often.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".