Mapping Natural Phenomena: Boreal Forest Fires with Non-discrete Boundaries
Bibliographic record
Abstract
Forest fires are spatially and temporally frequent in the boreal forest biome and continue to alter the spatial mosaic of its forest cover. Some of these fires occur in remote locations where direct socio-economic impacts are negligible, and are therefore not suppressed. However, these natural fires have many ecological consequences, and their monitoring and mapping therefore pose both an important and a challenging task. The current state of the art for fire-event mapping in remote northern Ontario is conducted at variable cartographic scales and generally relies on recording the approximate perimeters of the burned area from fixed-wing aircraft or helicopters with a handheld global positioning system receiver. All such techniques treat forest-fire boundaries, regardless of their detection and mapping resolutions or of the irregularity and gradient-like characteristics of their burned/not-burned interface, as crisp lines. Here we describe a procedure for standardizing the mapping of forest fires by an approach using high-spatial-resolution IKONOS satellite imagery that considers the actuality of gradual boundaries by assessing the fire-membership strength of each pixel prior to developing a footprint describing an individual fire event. Our case study is from northern Ontario, Canada, where the remote boreal forest fires are not regularly suppressed or monitored/mapped using traditional means. Furthermore, our analysis explores the sensitivity of this mapping effort to spatial resolution when describing measures of fire-footprint spatial geometry. We compare our mapping results with fire boundaries obtained by other means, using a series of overlap statistics to assess their spatial coincidence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".