Mortality assessment of moose (<i>Alces alces</i>) calves during successive years of winter tick (<i>Dermacentor albipictus</i>) epizootics in New Hampshire and Maine (USA)
Bibliographic record
Abstract
Populations within ecological communities constantly fluctuate due to a multitude of interactions that can be influenced by climate change. Moose (Alces alces (Linnaeus, 1758)) populations in northern New Hampshire and western Maine, subunits of the largest regional moose population in the continental United States, are suspected to be declining due to increasing frequency of winter tick (Dermacentor albipictus Packard, 1869) epizootics that cause >50% late-winter mortality of 9- to 12-month-old calves. To investigate this hypothesis, we collected general health measurements of calves captured at two study sites in January 2014–2016 and subsequently performed field necropsies and histologic examination of tissues of those radio-marked calves that died during winter and spring. At capture, calves (n = 179) were in normal (66%) and thin (32%) physical condition with high infestations of winter ticks. Most (88%) mortalities (n = 125) were associated with moderate to severe infestations of winter ticks. Gross necropsies and histologic examination found high tick infestations, emaciation, anemia, and endoparasitism; lungworm (species of the genus Dictyocaulus Railliet and Henry, 1907) was also found in most (87%) calves. Three consecutive years (2014–2016) of winter tick epizootics is unprecedented in the region, rare in North America, and arguably reflects a host–parasite relationship strongly influenced by climate change at the southern fringe of moose habitat.
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 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) | 0.000 | 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 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".