ReplyA re-examination of the effects of fire suppression in the boreal forest
Bibliographic record
Abstract
Ward and Tithecott (P.C. Ward and A.G. Tithecott. 1993. Ontario Ministry of Natural Resources, Aviation, Flood and Fire Management Branch, Publ. 305) presented data that indicated fire suppression activities in Ontario led to reductions in average annual area burned and greater numbers of small fires, compared with what would have been observed in the absence of suppression. Miyanishi and Johnson (K. Miyanishi and E.A. Johnson. 2001. Can. J. For. Res. 31: 14621466) have questioned aspects of that report, suggesting that the evidence does not demonstrate that suppression influences fire size or frequency. Fire-history studies in Ontario's forests and recent fire disturbance records do show that the fire-return interval has lengthened considerably in Ontario's protected forest since pre-suppression times. Analysis of forest inventory age-class distributions also reflect a reduction in overall forest disturbance rates in the past 40 years. Average annual burn fractions (ABF) calculated for protected and unprotected forests in northwestern Ontario for the period 1976-2000 show an ABF of 1.11% in the unprotected forest and only 0.34% in the protected forest. There is clear evidence that fire suppression in Ontario contains many fires at small sizes that would have otherwise grown to larger sizes, and reduces the overall average annual area burned in the protected forest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".