Increased overwinter mortalities of white-tailed deer (<i>Odocoileus</i> <i>virginianus</i>) fawns during a drought year
Bibliographic record
Abstract
Mortality rates of white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) fawns have been quantified throughout North America. Few studies, however, have assessed cause-specific mortality of fawns after the first 3 months of life or during a severe weather event. During 2010–2014, we captured and radio-tracked 93 fawns in southern and central Illinois and recorded 18 mortality events. In order of importance, survival rates were affected by days since capture, year of drought, age at capture, week after capture (1 or 0 indicator), and region. Estimated overwinter (fall through spring) survival rate (±SE) of fawns in both regions during 2010–2014 was 0.83 ± 0.04. However, estimated overwinter survival rates were depressed during 2012–2013, following the severe drought of 2012 (0.63 ± 0.11 or 0.66 ± 0.11 depending on the model). Main causes of mortality were capture-related and predation, though some dead deer also showed signs of hemorrhagic disease. We suspect that the extreme drought of 2012 created favorable conditions for fall–spring mortality of fawns, due to elevated disease transmission and lower forage quality and quantity for deer. In addition, drought may have contributed to predation by reducing abundance of alternative prey. Our results suggest that severe weather conditions during summer can substantially impact overwinter fawn survival.
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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 | 0.000 | 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) | 0.001 | 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".