Factors Affecting Pregnancy in Free-ranging Elk, <em>Cervus elaphus nelsoni</em>, in Michigan
Bibliographic record
Abstract
Uncertainty exists as to which factors are most closely related to probability of pregnancy in Elk (Cervus elaphus), which thresholds are key for managers who want to assess the potential productivity of free-ranging Elk herds, and whether these thresholds vary among populations. We examined relationships among pregnancy, age, and mass for 513 harvested free-ranging Elk in Michigan, and compared relationships with other published models and with thresholds derived from other free-ranging and penned populations to see if relationships were consistent among populations. Pregnancy rates varied (chi22 = 136.3; P < 0.0001) among yearling (0.30), prime-aged (2.5-11.5-year-olds; 0.88), and old (> 12.5-year-olds; 0.60) cows. Probability of pregnancy in adult cows was related to mass (chi2 = 7.4; P = 0.006), age (chi2 = 12.6; P = 0.0004) and age class (chi2 = 16.4; P < 0.0001), but not to lactation status (chi2 = 0.4; P = 0.515); pregnancy was also positively related (chi2 = 15.8; P < 0.0001) to mass in yearlings. Probability of pregnancy increased 1.02× and 1.04× for each 1 kg increase in body mass of adult and yearling cows, respectively, and prime-aged cows were 4.9× more likely to conceive than old cows. Compared to thresholds derived primarily from penned or farmed Elk, both adult and yearling free-ranging Elk in Michigan and elsewhere were able to achieve higher levels of pregnancy at lower body mass. Thresholds also varied among free-ranging Elk populations. Given variation among populations, managers should calibrate mass-pregnancy relationships for their respective populations to determine whether condition is potentially limiting pregnancy in their populations.
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 | 0.001 |
| 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".