Landsat-based analysis of insect outbreaks in southern Siberia
Bibliographic record
Abstract
Landsat data were used to examine the effect of large-scale insect outbreaks in the forests of southern Siberia. Two insect outbreaks were studied: Ket‐Chulym, ~1.5 million hectares of forest damaged between 1954 and 1957; and Priangar'e, ~0.5‐0.7 million hectares of forest damaged between 1994 and 1996. Landsat scenes from 1980 to 2000 were analyzed. The optimal Landsat channels combination for detecting damage classes were bands 2 (0.525‐0.605 μm), 4 (0.750‐0.900 μm), 5 (1.55‐1.75 μm), and 6 (10.40‐12.50 μm). The damage to the forests caused an increase in radiometric temperature (20.20 ± 0.04°C for damaged forests versus 19.47 ± 0.02°C for healthy forests). The following pattern of forest succession was observed in the outbreak areas: dead stands with dense grass and shrub communities, burn scars, grass and shrub formations, open woodlands, closed young and middle-age birch stands, mature birch stands, and mixed conifer‐deciduous stands. Forest regeneration goes through long-term species-change successions, and the rate depends on the size of the outbreak area. On-ground mapping after the outbreak and later Landsat analysis showed that even 45 years after the Ket‐Chulym outbreak the area of forests did not increase. In the Priangar'e area approximately 45% of damaged forests have recovered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".