An assessment of expert-based marten habitat models used for forest management in Ontario
Bibliographic record
Abstract
We used marten snow tracking data and a previously developed empirical habitat model from northeastern Ontario to validate a number of expert-based, non-spatial marten habitat models. In particular, we tested the non-spatial Ontario Wildlife Habitat Analysis Model, the Boreal East Habitat Suitability Matrix (including tests of both standard forest units and development stages), and Allen's (1982) HSI model. Marten habitat use as measured by tracks in the snow was consistent with predictions of all the expert-based models, suggesting that these models correctly characterized the stand-level forest cover selected by marten in winter. Suitability ranks for individual stands derived from standard forest units and development stages also were consistent with their use by marten. The empirical model was consistent with the expert-based models in that it considered suitable forest stands to be those with tall trees dominated by spruce (Picea spp.) and balsam fir (Abies balsamea) trees, with a large amount of coarse woody debris, and high canopy closure. Our findings suggested that the expert-based models were able to characterize stand structure used by marten despite some of the models using only inputs available from stand inventories. This was accomplished because stand structural elements such as coarse woody debris were integrated into OWHAM and HSM indirectly, through relationships with stand age and species composition. Key words: boreal forest, forest inventory, habitat, habitat suitability, guidelines, Forest Ecosystem Classification, landscape, Martes americana, resource selection, snow tracking, spatial autocorrelation, stand structure
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".