Modeling stand fire probabilities with unobserved heterogeneity. Estimating stand age and climate change effects in Chilean radiata pine plantations
Bibliographic record
Abstract
Evenly managed forest plantations are potentially vulnerable to fires because of their high fuel load build-up in each rotation. We use panel data analysis that considers the possible correlation between the observed covariates of interest with spatial unobserved heterogeneity. We compared two alternative approaches to estimate the stand burn probability function of the stand age: the average structural function (ASF) and the local average structural function (LASF). While our results show a significant positive effect of the stand age for the mature stage under both approaches, more differentiation in the stand burn probability for different ages is captured with the LASF. Also, under the LASF, the stand age functions are more sensitive to changes in site productivity. We predicted the burned area under the RCP 4.5 and RCP 8.5 climate scenarios considering adaptation in management regimes to site productivity changes. The largest impact is projected for the coastal areas where site productivity increases are combined with more suitable climate conditions for flammability. For the dryer hinterland, however, stand burn probabilities and the burned area are predicted to decline in the second period and the RCP 8.5 because of the dominant negative effect resulting from the site productivity reduction.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".