Impact of stand structure on surface fire ignition potential in <i>Picea abies</i> and <i>Pinus sylvestris</i> forests in southern Finland
Bibliographic record
Abstract
Forest fire ignition potential was investigated in Picea abies (L.) Karst. (Norway spruce) and Pinus sylvestris L. (Scots pine) dominated stands of 0, 15, 3045, and 4060 years of age. A series of small-scale (<0.5 m2) ignition tests were carried out in experimental plots on 61 different days in June, July, and August. Ignition success percentages were analyzed in relation to stand structural properties, preclassified stand types, and the output of the Canadian Fire Weather Index system. In addition, the number of average stand-type-specific fire days was estimated based on weather data (JuneAugust) for southern Finland for the years 19912002. Factors in stand structure that significantly correlated with the ignition success percentage were canopy depth and leaf area index, the correlation coefficients being 0.575 (p < 0.005) and 0.582 (p < 0.005), respectively. In Pinus sylvestris dominated stands, ignition tests produced self-sustained surface fires in 32.0%, 24.0%, and 19.3% of cases in 0-, 15-, and 30- to 45-year age classes, respectively. In Picea abies dominated sites conditions were favorable for fire in 12.0% and 4.6% of trials in the 0- and 40- to 60-year age classes, respectively. The output of the FWI-system correlated well with the ignition success in June and July but poorly in August. Based on the 12-year time series analysis, there were on average per year 27, 18, and 14 potential fire days in 0-, 15-, and 30- to 45-year-old Pinus sylvestris stands, and 10 and 4 potential fire days in 0- and 40- to 60-year-old Picea abies stands, respectively. We concluded that the dominance of Picea abies or Pinus sylvestris and stand age appear to modify the ignition conditions significantly and should be used as threshold indicators in fire occurrence predictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".