A test of Ontario's Habitat Suitability Matrix as a forest management planning tool for forest birds
Bibliographic record
Abstract
We used point-count data collected as part of Bird Studies Canada's Boreal Forest Bird Program to validate habitat suitability models for 22 forest bird species in Ontario's Habitat Suitability Matrix. We found that many of the species'models performed relatively poorly in discriminating between occupied and unoccupied sites, primarily due to the high error of commission rates (false positive predictions). Since species presence and abundance were assessed by single, five minute point counts, insufficient sampling was at least partly responsible for some of the observed over-prediction. Results suggested that model parameters for at least nine of the species tested (hairy woodpecker [Picoides villosus], blueheaded vireo [Vireo solitarius], red-eyed vireo [Vireo olivaceus], red-breasted nuthatch [Sitta canadensis], Swainson's thrush [Catharus ustulatus], hermit thrush [Catharus guttatus], Tennessee warbler [Vermivora peregrina], Blackburnian warbler [Dendroica fusca] and dark-eyed junco [Junco hyemalis]) should be reviewed to improve the predictive capability of the models and to ensure appropriate consideration of the habitat needs of these species during forest management planning. Key words: boreal forest, forest birds, discrimination capacity, habitat models, habitat suitability matrix, model accuracy, model validation, relative operating characteristic curve, ROC
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".