A boreal wildfire and harvesting database with ensemble confidence attributes for Ontario (1972–2021+)
Bibliographic record
Abstract
We present a repeatable workflow that produced a comprehensive wildfire and timber harvesting database for Ontario (1972–2021) that accommodates annual updates after each new fire season. Training sites for classification are identified at the individual scene-level to avoid spectral variations introduced by time and distance. ISODATA classification on the training data produces clusters that are modelled by a smooth polynomial function to identify a local minimum point along the classification clusters that distinguishes disturbances from non-disturbances. This threshold is then applied to map disturbances on independent scenes of Landsat MSS, TM, ETM+, or OLI imagery. Results are aggregated to 1.44 ha cells and converted to points for dissemination; we do not map explicit boundaries to avoid issues of context- and scale-dependence, or the realities of transitional boundaries. Disturbance points are cross-referenced through time, ensuring that the earliest date for each disturbance is recorded. Disturbance points are intersected with other harvesting and fire databases to assess their ensemble confidence which is attached to each mapped location. We present summaries of Ontario’s boreal disturbance mapping with respect to levels of assessed confidence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".