Landscape-level interactions of prefire vegetation, burn severity, and postfire vegetation over a 16-year period in interior Alaska
Bibliographic record
Abstract
Landsat imagery was used to study the relationship between a remotely sensed burn severity index and prefire vegetation and the postfire vegetation response related to burn severity within a 1986 burn in interior Alaska. Vegetation was classified prior to the fire and 16 years after the fire, and a chronosequence of remotely sensed vegetation index values was analyzed as a surrogate of vegetation recovery. Remotely sensed burn severity varied by vegetation class, with needle-leaf forest classes experiencing higher burn severity than broadleaf forest or broadleaf shrubland classes. Burn severity varied by cover within needle-leaf classes. Elevation also had an influence on burn severity, presumably as a result of there being less fuel above the treeline. Several large broadleaf areas at the fire perimeter appeared to act as fire breaks. A remotely sensed vegetation index peaked 814 years after the fire, and increase in the vegetation index was highest within the highest burn severity class. Self-replacement appeared to be the dominant successional pathway, with prefire needle-leaf forest classes mostly succeeding to needle-leaf woodland and with prefire broadleaf forest mostly succeeding to broadleaf shrubland. Because the remotely sensed indices were based on reflected solar radiation, they are likely indicative of surface properties, such as canopy destruction and surface charring, rather than subsurface properties, such as postfire depth of organic soil.
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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 | 0.000 | 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) | 0.001 | 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".