Post-Fire Canopy Height Recovery in Canada’s Boreal Forests Using Airborne Laser Scanner (ALS)
Bibliographic record
Abstract
Canopy height data collected with an airborne laser scanner (ALS) flown across unmanaged parts of Canada’s boreal forest in the summer of 2010 were used—as stand-alone data—to derive a least-squares polynomial (LSPOL) between presumed post-fire recovered canopy heights and duration (in years) since fire (YSF). Flight lines of the > 25,000-km ALS survey intersected 163 historic fires with a known day of detection and fire perimeter. A sequential statistical testing procedure was developed to separate post-fire recovered canopy heights from pre-fire canopy heights. Of the 153 fires with > 5 YSF, 121 cases (89%) could be resolved to a complete or partial post-fire canopy replacement. The estimated LSPOL can be used to estimate post-fire aboveground biomass and carbon sequestration in areas where alternative information is dated or absent. These LIDAR derived findings are especially useful as existing growth information is largely developed for higher productivity ecosystems and not applicable to these ecosystems subject to large wildfires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".