Potentially limited detectability of short-term changes in boreal fire regimes: a simulation study
Bibliographic record
Abstract
Climate change is expected to increase area burned in the boreal plains ecozone of Canada in the early 21st century (2001–50). I examined the influence of inter-annual variability in area burned and short observed time series on the probability of detecting if an increase has occurred, using a null model of present and future fire regimes. A wide range of fire cycles are consistent with annual area burned in the late 20th century (1959–99). Fire cycles estimated from the reciprocal of the average annual burn fraction over a 50-year period are not very precise, and overestimate the fire cycle if years with large annual area burned have not recently occurred. Under the default assumptions, the probability of detecting a doubling of annual area burned during 2001–50 is 73% if it occurred instantaneously, but only 31% if it occurred gradually. Imprecise estimates and uncertainty in the ability to detect changes in fire cycles poses challenges for implementing aspects of sustainable forest management. Alternate empirical or model-based statistics, such as return periods for annual areas burned of a given magnitude, may be useful for inferring frequencies and magnitudes of large fire years that have not yet been observed.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".