Habitat suitability modeling for mink passage activity: A cautionary tale
Bibliographic record
Abstract
ABSTRACT Many studies have evaluated wildlife passage effectiveness, but few have explored how accurately passage activity can be modeled. I created a habitat suitability index (HSI) model for American mink ( Neovison vison ) using a geographic information system with 17 wildlife passages located in Quebec, Canada as validation for the model. I addressed how well HSI modeling using constrained habitat data could determine mink passage activity and tested model sensitivity to different parameterizations. Uncertainty analysis revealed that the HSI model was sensitive to extreme changes in factor weights and scale. I used a generalized linear model to test how well the constrained HSI model explained the variability in passage activity (counts). For the HSI model (and all alternatives) the HSI scores were negatively associated with passage use. The predictive power of all models greatly improved after including aspects related to passage construction, with the pseudo‐ R 2 increasing by 64–73%. These findings suggest that the constrained HSI models are a poor predicator of passage activity for mink, but wildlife passage characteristics are highly predictive. Transportation agencies would benefit from the ability to make informed planning decisions; however, greater care is required to determine passage suitability. The proper implementation of these tools requires knowledge of habitat preferences and how movement is influenced by the wildlife passages themselves. © 2017 The Wildlife Society.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".