Canada goose (branta canadensis) survival and harvest rates in developed and rural landscapes of central Indiana & urban Canada goose management research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it