Comparison of fire scars, fire atlases, and satellite data in the northwestern United States
Bibliographic record
Abstract
We evaluated agreement in the location and occurrence of 20th century fires recorded in digital fire atlases with those inferred from fire scars that we collected systematically at one site in Idaho and from existing fire-scar reconstructions at four sites in Washington. Fire perimeters were similar for two of three 20th century fires in Idaho (1924 and 1986). Overall spatial agreement was best in 1924 (producer’s accuracy = 94% and 68% and user’s accuracy = 90% and 70% for the 1924 and 1986 fires, respectively). In 1924, fire extent from the atlas was greater than for fire scars, but the reverse was true for 1986. In 1986, fire extent interpreted from the delta normalized burn ratio derived from pre- and post-fire satellite imagery was similar to that inferred from the fire-scar record (producer’s accuracy = 92%, user’s accuracy = 88%). In contrast, agreement between fire-scar and fire-atlas records was poor at the Washington sites. Fire atlases are the most readily available source of information on the extent of late 20th century fires and the only source for the early 20th century. While fire atlases capture broad patterns useful at the regional scale, they should be field validated and used with caution at the local scale.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".