Overlapping Landsat Scene Classifications and Focal Context to Identify Boreal Disturbance Mapping Uncertainty
Bibliographic record
Abstract
ABSTRACT The BorealDB dataset provides annual fire and timber harvesting disturbance classifications for Ontario that are derived from a collection of independently classified Landsat scenes. This study assesses the confidence of BorealDB classifications within overlapping scene margins since multiple classifications for common locations are available. For each focal point in BorealDB , the disturbance state of its four nearest spatial orthogonal neighbors were extracted and used to produce classification tree (CT) and random forest (RF) predictions of the focal class. Uncertainty was assessed as being greatest when predictions by neighboring locations or overlapping disturbance classes disagree with the focal class. The assessment found that identified locations of uncertainty within BorealDB varied with disturbance class, with fire having lower uncertainty than timber harvesting. With the results of the analysis, we recommend the inclusion of the analysis outputs and comparisons to supplement existing ensemble confidence attribute in BorealDB .
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".