Effects of interaction between forest structure and precipitation event characteristics on fuel moisture conditions
Bibliographic record
Abstract
Estimating forest fire danger is of primary concern for the Austrian forest fire management. The fine fuel moisture code (FFMC) of the Canadian Fire Weather Index (CFWI) is used for determining ignition danger. The FFMC is calculated by using the Integrated Nowcasting through Comprehensive Analysis (INCA) system, which provides interpolated weather parameters, available at 1 km2 spatial resolution. Automated fuel sticks were used for measuring microclimate-defined moisture content from 2018 to 2020 in two differently structured sites: closed forest and forest gap. A remotely automated weather station (RAWS) measured the meteorological parameters of the research area. First, the capability of an interpolated large-scale FFMC to capture local moisture conditions was studied. Second, the effects of an interaction between forest structure and precipitation event characteristics on moisture content behaviour were investigated. Bayesian-based techniques in dependence of the three consecutive years were applied. Our results show that the correlations between INCA FFMC and RAWS FFMC are high (0.80–0.95) and INCA FFMC is capable of capturing the local microclimatic variations (0.68–0.78). Correlations ranging from 0.74 to 0.86 were evidenced between the fuel stick FFMC values of the forest and the gap, revealing that INCA FFMC cannot capture the microclimate induced differences between the two forest conditions. We found that: (i) the buffering capability of forest structure slows the absorption rate by 1.8 to 3.4%/h, (ii) absorption rate difference between the forest and the gap was lowest (1.8%/h) in the case of short and heavy rain, (iii) during the desorption phase the moisture content remained similar for both the closed forest and the gap. Overall, the structure induced buffering capability was strongest in the case of short and light rain. Longer and more intensive precipitation events, especially after canopy saturation during throughfall, are likely to lessen the buffering capability of the forest.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".