A comparison of greenness estimates from the tassled cap transformation and normalized difference vegetation index as a component in habitat selection models for grizzly bears.
Bibliographic record
Abstract
Improving habitat-selection models for grizzly bears is important to the conservation and management of the species. Remote-sensing data can be used to derive vegetation indices from Landsat TM, a surrogate for habitat quality. This study compared and evaluated Tasseled Cap Analysis (TCA) 'Greenness' and NDVI based on grizzly bear habitat selection, derived from radio-telemetry, in central British Columbia. Four groups of habitat models were developed for the mountain and plateau portions of the study area. AIC was used to rank the models and a k-fold cross validation to evaluate these competing models. One model using TCS greenness and a 21x21-pixel buffer with data from mountainous regions had the best predictive ability. Bears in the mountains are more dependant on vegetation as a food source, which is reflected in their selection for areas of higher greenness compared to plateau bears which have more access to ungulates and have larger home range sizes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".