Net primary productivity following forest fire for Canadian ecoregions
Bibliographic record
Abstract
Recent modelling results indicate that forest fires and other disturbances determine the magnitude of the Canadian forest carbon balance. The regeneration of post-fire vegetation is key to the recovery of net primary productivity (NPP) following fire. We geographically co-registered pixels classed using the Boreal Ecosystem Productivity Simulator, a process-based model with AVHRR (advanced very-high resolution radiometer) satellite estimates of leaf-area index and land cover type, with polygons from a recent database of large Canadian fires. NPP development with time since fire was derived for the first 15 years following the disturbance in the boreal and taiga ecozones. About 7 × 10 6 ha were analysed for over 500 fires occurring between 1980 and 1994. NPP increases linearly through this period, at rates that depend on ecoregion. A longer data set for the Boreal Plains ecozone of Alberta shows that NPP levels off at about 20-30 years and remains constant for 60 years. The NPP trajectories can be used as spatial averages to support models of forest carbon balance and succession through the most fire-prone regions of Canada.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".