Augmenting the existing survey hierarchy for mountain pine beetle red-attack damage with satellite remotely sensed data
Bibliographic record
Abstract
Estimates of the location and extent of the red-attack stage of mountain pine beetle (Dendroctonus ponderosae Hopkins) infestations are critical for forest management. The degree of spatial and temporal precision required for these estimates varies according to the management objectives and the nature of the infestation. This paper outlines the range of information requirements associated with mountain pine beetle infestations, from the perspectives of forest inventory, planning, and modeling. Current methods used to detect and map red-attack damage form a hierarchy of increasingly detailed data sources. The capability of satellite-based remotely sensed data to integrate into this hierarchy and provide data that is complementary to existing survey methods is presented, with specific examples using medium (Landsat) and high (IKONOS) spatial resolution imagery. The importance of matching the information requirement to the appropriate data source is emphasized as a means to reduce the overhead associated with data collection and processing. Key words: mountain pine beetle, red-attack, remote sensing, detection, Landsat, IKONOS
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".