REVISITING THE RECRUITMENT-MORTALITY EQUATION TO ASSESS MOOSE GROWTH RATES
Bibliographic record
Abstract
Hatter and Bergerud (1991) developed a recruitment-mortality (R-M) equation to estimate the annual finite rate of change (λ) in a moose ( Alces alces ) population from a single estimate of calf recruitment and adult mortality. I present and assess an alternative formulation of the R-M equation and compare it with the original. A modification to the R-M equations is provided to accommodate early to mid-winter composition surveys where recruitment is measured when calves are less than 1 year-of-age. An example with the modified R-M equation illustrates estimation of λ for the female component of two moose populations under recent study in British Columbia, Canada. Due to potential biases with estimating recruitment and mortality rates, the calculation of λ with the R-M equation should be verified with periodic density surveys whenever possible. The R-M equation is most useful for estimating λ when moose density surveys are not feasible or an estimate of the adult survival rate is available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".