Using Landsat imagery to backcast fire and post-fire residuals in the Boreal Shield of Saskatchewan: implications for woodland caribou management
Bibliographic record
Abstract
Boreal woodland caribou (Rangifer tarandus caribou) are designated as threatened under Canada’s Species at Risk Act. A Recovery Strategy for the boreal population of caribou identified critical habitat for all but 1 of 51 caribou ranges – Saskatchewan’s Boreal Shield (SK1). The strategy identified 65% undisturbed habitat as the threshold below which a local population was not likely to be self-sustaining. Disturbance was measured as the combined effects of fires <40 years and anthropogenic land use. The fire component of the total disturbance model used fire polygons that were delineated using traditional mapping methods. Our study maps fire from 1988–2013 using the differenced Normalized Burn Ratio analysis of Landsat Thematic mapper and Operational Land Imager. Annual burned areas based on fire perimeters were similar between traditionally and Landsat-derived inventory approaches, but the traditional methods overestimated within-burn areas by 31.8%, as a result of including post-fire residuals and water bodies as burned. The federal recovery model assumes that all lands within provincial fire polygons (<40 years) are inadequate as caribou habitat, and ignores the potential value of post-fire residuals and water bodies as habitat. For some Boreal Shield ranges including SKI, where fire comprises the majority of the total disturbance and residual patches are abundant, total disturbance calculations, critical habitat designation and range planning decisions should take into account residuals, including water bodies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".