A habitat suitability index model to assess moose habitat selection at multiple spatial scales
Bibliographic record
Abstract
We developed a habitat suitability index (HSI) model for moose (Alces alces) in the boreal forest. The model used two components: a suitability index for food (SI food ) and another for the interspersion between cover and food (SI edge ). We used forest maps as the input data source, and the value of each stand type in terms of cover and food was based on field surveys. To validate the model, the habitat preference of moose equipped with global positioning system telemetry collars was assessed at both landscape and home-range scales. We expected the habitat-preference index to correlate with suitability indices determined using the global model and each of its two components. Habitat suitability was assessed in evaluation plots of 500, 100, and 10 ha. Unexpectedly, the habitat-preference index correlated better with SI food and SI edge than with the global model. The suitability indices also performed better when assessed in large plots. Selection of 500 ha plots related mostly to SI edge , but SI food was more important when smaller evaluation plots were used, especially for males. Females preferred plots with intermediate SI food values. At the fine scale, SI edge was not as attractive to moose as was previously observed, presumably because snow conditions prevailing in our study area were relatively moderate. We recommend utilizing the model with SI edge in large plots (ca. 500 ha) and SI food in smaller plots. Our model could be adapted and applied to other areas by using empirical data to adjust the relative value of stand types in terms of cover and food.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".