Erratum: Economic analysis of health effects from forest fires
Bibliographic record
Abstract
A computational error has been discovered in the way that threshold levels were incorporated in the calculation of the health impacts and the associated calculation of conservative levels of health and monetary impacts.This resulted in an overestimate of the effects; the corrected effects are approximately 25% of the original estimates.Table 3 presents the results with the correct incorporation of the 30 mg/m 3 threshold (no health effects below this level) and is the set of mean estimates from the simulation with only acute (particularly minimum mortality risk) levels of health impacts and the distribution of monetary value estimates as described in the paper.The range of these estimates for the monitoring station results are ($5.1 million 10th percentile) to ($1.7 million 90th percentile), while the range for the smoke dispersion models are ($3.8 million 10th percentile) to ($1.2 million 90th perecentile).These values represent the most conservative estimates on thresholds and health risks.In comparison, if a typical ambient baseline level of PM 2.5 of 12 mg/m 3 were used in the calculation, then total impacts would be estimated at $4 421 703 for the smoke dispersion model and $5 064 438 for the monitoring station model.The results place the impact of air quality changes at approximately the same level as the lost homes and buildings and damages to bridges (Table 4 in the paper), but much lower than the loss in timber supply.Qualitatively, the conclusion that air quality impacts can be potentially significant as a portion of impacts from fire remains.The authors express regret for any inconvenience caused by this error and apologize for any difficulties this has caused.The first two authors accept full responsibility for the error.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.018 |
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".