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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".