Timing and causes of mortality in the endangered Vancouver Island marmot (<i>Marmota vancouverensis</i>)
Bibliographic record
Abstract
We used radiotelemetry to evaluate seasonal survival rates and mortality factors for a critically endangered island endemic, the Vancouver Island marmot (Marmota vancouverensis Swarth, 1911). Recovery of radio transmitters and marmot remains suggested that predation was the major cause of mortality, accounting for at least 24 of 29 (83%) known-fate deaths recorded since radiotelemetry efforts began in 1992. Wolves (Canis lupus L., 1758) and cougars (Puma concolor (L., 1771)) apparently accounted for 17 deaths (59%). Three marmots (10%) were killed by golden eagles (Aquila chrysaetos (L., 1758)), four (14%) were killed by unknown predators that probably included all of the above species, two (7%) died from unknown causes, and three (10%) died during hibernation in a single burrow. Mortality rates varied seasonally. The daily probability of death during hibernation was very low (Pdeath = 0.016). The probability of death was also low from spring emergence through 31 July (Pdeath = 0.051), but was eight times higher in August (Pdeath = 0.395) and four times higher in September (Pdeath = 0.175). We concluded that predation was the proximate cause of recent declines in wild Vancouver Island marmot populations, that losses were highly concentrated in late summer, and that previous studies exaggerated the importance of winter mortality. We suggest that high predation rates were associated with forestry and altered predator abundance and hunting patterns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".