Nasal Bots and Lice from White-tailed Deer in Southern Alberta, Canada
Bibliographic record
Abstract
Heads of 64 white-tailed deer (Odocoileus virginianus) fawns, harvested in the vicinity of Magrath, Alberta, Canada, (49 degrees 24'782''N, 112 degrees 52'113''W) were examined for the presence of nasal bots and lice. The deer were collected between 8-30 January 2004 as part of a government-approved herd reduction protocol. The entire surface of each head was scanned visually for the presence of lice. Each head was split longitudinally, and the nasal passages, sinuses, and ethmoid region were washed for recovery of nasal bots. First instar Cephenemyia spp. were recovered from 17 heads (27%). Intensity of infestation ranged from 1-18 larvae (mean intensity 4.8). Among fawns, there were no significant differences in prevalence or mean intensity between the sexes. Two species of nasal bots were identified. Smaller larvae, tentatively identified as C. jellisoni, were present in 16 of 17 infested deer while larger specimens, tentatively identified as C. phobifera, were found in four deer; and in three of the four it co-occurred with C. jellisoni. The presence of C. phobifera in Alberta would represent a range extension for this species, which has not been known to occur west of North Dakota. Thirty-one fawns (48%) were infested with the sucking louse Solenopotes ferrisi. One infested fawn also had one specimen of the chewing louse, Tricholiopeurus lipeuroides.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".