Data and R computer code from: Summer elk calf survival in a partially migratory population
Bibliographic record
Abstract
These data and computer code (written in R, https://www.r-project.org) were created to statistically evaluate a suite of intrinsic and extrinsic risk factors related to calf elk and their mothers' body condition and age. Specifically, known-fate data were collected from 94 elk calves monitored from 2013-2016 in a partially migratory elk (Cervus canadensis) population in Alberta, Canada. Along with adult female data on pregnancy status, age, and body condition, we created a time-to-event dataset that allowed us to analyze calf mortality risk in a time-to-event approach. We also estimated pooled survivorship and cause-specific mortality, as well as stratifying these metrics by migration tactic (resident vs. eastern migrant). Cox proportional hazards models were used to evaluate calf mortality risk in terms of forage biomass (kg/ha), bear predation risk (from an RSF), and other factors that varied between migration tactics. We tested for differences in a number of maternal reproductive parameters (e.g., pregnancy status) and for calf explanatory variables between migrant and resident elk segments. We also use cumulative incidence functions to estimate cause-specific mortality in this multiple carnivore system. Ultimately, we hope that this work helps wildlife managers anticipate how elk calf survival and partial migration dynamics are affected by grizzly bear predation, and our study builds on a long-term partial migration study at the Ya Ha Tinda Ranch in Alberta, Canada.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.062 |
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".