Canada goose (branta canadensis) survival and harvest rates in developed and rural landscapes of central Indiana & urban Canada goose management research
Bibliographic record
Abstract
This research project has presented research and a proposed methodology aimed at studying \nthe survival and harvest rates of Canada goose (Branta canadensis) populations. Additionally, this study \nproposes methods for comparing these rates between urban and rural populations of Canada geese. \nThis is accomplished by pooling data from both populations relating to banding and direct recovery rates \nwhereby annual survival estimates can be made via the program MARK available in the RMark package \nwith a joint live-dead recovery model. Models were then designed to incorporate predetermined \ncovariates and then fitted to assess for differences in survival between urban and rurally banded \nindividuals. Model estimated rates for annual survival and direct recovery were then used to calculate \nannual harvest rates for the populations. Models are then able to be evaluated by performing a \nlikelihood ratio test, to determine if two models differ based on the impact of time-varying covariates \nupon the overall variance within the models. These simulations will then be repeated 1,000 times for \neach model comparison. Comparisons of rural and urban goose survival and harvest rates may allow for \na more informed management approach for the species, especially in urban environments where \nhunting is often not a feasible management option.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".