Bibliographic record
Abstract
Die-offs of moose (AIces alces) associated with, or attributed to, winter ticks (Dermacentor albipictus) are widespread and have been reported since the early part of the last century. Extrinsic factors such as weather and vegetative structure, and host factors such as moose density and, indirectly, tick-induced damage to the hair coat, were examined in an attempt to predict related problems for moose. The proposal that warmer and shorter winters result in increased survival of adult female ticks dropping off moose in March and April, and increased tick populations on moose the following winter, was generally confirmed. Annual changes in hair damage and loss on moose, which are documented from the air, coincided with annual changes in numbers of ticks on moose, providing managers with a survey tool to monitor and estimate changing numbers of ticks. Tick numbers lagged 1 year behind moose numbers in Elk Island National Park over a 12-year period, and many moose died when numbers of both were high. Several widespread, concurrent die-offs suggest extrinsic influences play a role, possibly independent of moose density. The lack of objective and continuous data sets should guide future research efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".