Modelling fisher (martes pennanti) habitat associations in Nova Scotia
Bibliographic record
Abstract
A slower rate of population growth in one of two fisher (Martes pennanti, Erxleben 1777) populations in Nova Scotia raised questions concerning limitations to the expansion of the western population. The current study addressed Nova Scotia Department of Natural Resources' need to increase understanding of fisher habitat associations within the province as well as to explore the limitations of using pre-existing data to model those associations. I fit multivariate logistic regression models of fisher presence and absence using explanatory variables from existing Geographic Information System (GIS) forestry data in three counties in Nova Scotia where the fisher population is well established. Comparing predictions made by applying coefficients from the best-fit model based on the entire data set, under cross-validation conditions and using an independent data set respectively showed correct classification rates of 70%, 68% and 62%. The modelling procedure was then used to predict areas of high and low quality habitat in restocking target zones not currently occupied by fishers. Chi-square tests of movement variables calculated for radio-tracked fishers were used to empirically test for differences in the response of released fishers to resident and restocking areas and to low and high quality habitat within restocking areas. Differences in the response of fishers to resident and restocking areas were detected in 5 of 7 movement variables at a significance level of p<0.1. No effect of high and low quality habitat on fisher movement was detected, suggesting the models' limited ability to predict high quality habitat.
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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 | 0.000 | 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) | 0.005 | 0.005 |
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; both teacher heads agree on what is shown here.
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".