GIS‐Generated, Expert‐Based Models for Identifying Wildlife Habitat Linkages and Planning Mitigation Passages
Bibliographic record
Abstract
Abstract: We developed three black bear ( Ursus americanus ) habitat models in the context of a geographic information system to identify linkage areas across a major transportation corridor. One model was based on empirical habitat data, and the other two (opinion‐ and literature‐based) were based on expert information developed in a multicriteria decision‐making process. We validated the performance of the models with an independent data set. Four classes of highway linkage zones were generated. Class 3 linkages were the most accurate for mapping cross‐highway movement. Our tests showed that the model based on expert literature most closely approximated the empirical model, both in the results of statistical tests and the description of the class 3 linkages. In addition, the expert literature–based model was consistently more similar to the empirical model than either of two seasonal, expert opinion–based models. Among the expert models, the literature‐based model had the strongest correlation with the empirical model. Expert‐opinion models were less in agreement with the empirical model. The poor performance of the expert‐opinion model may be explained by an overestimation of the importance of riparian habitat by experts compared with the literature. A small portion of the empirical data to test the models was from the pre‐berry season and may have affected how well the model predicted linkage areas. Our empirical and expert models represent useful tools for resource and transportation planners charged with determining the location of mitigation passages for wildlife when baseline information is lacking and when time constraints do not allow for data collection before construction.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".