Relationship between winter severity and survival of mule deer fawns in the Peace Region of British Columbia
Bibliographic record
Abstract
This extension note summarizes results of an ongoing study to measure the survival of mule deer ( Odocoileus hemionus) fawns through their first winter in the Peace Region of British Columbia. Each spring since 1991, mule deer were counted and classified by sex and age by driving along roads that go through areas known for providing good winter range. Seven transects were driven for a total distance of 205.3 km. The number of fawns observed is expressed as a ratio of fawns per 100 does. Average monthly air temperature and total monthly snowfall data from November to the following April from 1991 through 2008 were obtained from Environment Canada’s website. A winter severity index (WSI) integrating temperature and snowfall data was calculated and correlated to the observed fawn-to-doe ratio. A statistically significant relationship between this ratio and the WSI was obtained through regression analysis. Resulting data confirms previous research that showed survival of fawns through their first winter is higher in milder winters. For the same time period, we also compared WSI values for the Peace Region with those from three other areas in British Columbia. Results indicate that, on average, the Peace Region experienced harsher winter conditions than the other regions. The variation of the WSI was also much greater in the Peace Region. These results have implications for mule deer management in this region.
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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 | 0.001 | 0.000 |
| 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".