Detecting and mapping mountain pine beetle red-attack damage with SPOT-5 10-m multispectral imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study was to gauge the effectiveness of using SPOT-5 10-m multispectral imagery to detect and map red-attack damage for an area near Cranbrook, British Columbia, Canada. A logistic regression model was used to incorporate SPOT imagery with elevation and associated derivatives for redattack detection and mapping. Separate independent sets of calibration and validation data, collected via a detailed aerial survey, were used to train the classification algorithm and vet the output maps of red-attack damage. The output from the logistic regression model was a continuous surface indicating the probability of red-attack damage. Using a greater than 50% probability threshold, red-attack was mapped with 71% accuracy (with a 95% confidence interval of ?9%). This level of accuracy is comparable to that achieved with Landsat single-date imagery in an area with similar levels of infestation. If a synoptic view of mountain pine beetle red-attack damage at the landscape level is required, and if Landsat data are unavailable, SPOT-5 10-m multispectral imagery may be considered an alternative data source, albeit an expensive one, for detecting and mapping mountain pine beetle red-attack damage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it