GROOMING AND RUBBING BEHAVIOR BY MOOSE EXPERIMENTALLY INFESTED WITH WINTER TICKS (DERMACENTOR ALBIPICTUS)
Bibliographic record
Abstract
Rates of grooming, rubbing, and shaking were observed of 12 moose (Alces alces) infested with 4 levels of winter ticks (Dermacentor albipictus) and 5 uninfested control animals. Modes of grooming varied among moose and occurred with the tongue, hind feet, head, ears, antlers, teeth, and neck. Only moose with ticks used teeth and ears to groom. Uninfested moose and moose prior to being infested groomed and rubbed little. Grooming was greater immediately following than before infestation, and initial grooming and rubbing were predominant at the sites of infestation. Grooming declined in mid-winter months when nymphs develop slowly and increased in late winter and early spring when nymphs and adults actively feed; rubbing only increased in late winter and early spring. Cumulative grooming-rubbing was positively correlated with level of tick infestation and hair loss, and negatively correlated with end body weight of female calves only. Intense individual bouts of grooming and rubbing during April lasted 13–141 min. Over the entire study, cumulative grooming-rubbing in daylight hours for moose with 21,000–42,000 larvae equaled 6–28 d (μ = 12.7), and from February to April moose with 42,000 ticks groomed and rubbed on average ≥5.0–7.5 min/h. The removal of ticks was high (77–96%) indicating that grooming and rubbing are positive behavioral responses with respect to reducing the impact of winter ticks.
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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 | 0.001 | 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) | 0.001 | 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".