Analyzing a North American prairie wildfire using remote sensing imagery
Bibliographic record
Abstract
Grassland wildfires have profound immediate effects on ecosystems. A wildfire that occurred in Grasslands National Park (GNP) on April 27th2013 has severely disturbed the local ecosystem. This study was thus conducted to evaluate impacts of the fire on this semi-arid grassland. Spectral indices including Normalized Burn Ratio (NBR), Mid-Infrared Burn Index (MIRBI), and Normalized Difference Vegetation Index (NDVI), derived from Landsat images were explored to evaluate the burn severity, to assess the relationship between pre-fire vegetation parameters and burn severity, and to analyze the vegetation recovery progress. Results indicated that while the selected spectral indices were all able to detect burn severity, the MIRBI showed the best performance. Severely burned areas were distributed along a river where a large amount of pre-fire dead biomass had accumulated. Overall the grassland ecosystem showed a strong resilience by recovering quickly after the fire.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".