Moisture content variation of ground vegetation fuels in boreal mesic and sub-xeric mineral soil forests in Finland
Bibliographic record
Abstract
Forest fire risk in Finland is estimated with the Finnish Forest Fire Index (FFI), which predicts the fuel moisture content (FMC) of the forest floor. We studied the FMC variation of four typical ground vegetation fuels, Pleurozium schreberi, Hylocomium splendens, Dicranum spp., and Cladonia spp., and raw humus in mature and recently clear-cut stands. Of these, six were sub-xeric Pinus sylvestris stands, and six mesic Picea abies stands. We analysed the ability of the FFI to predict FMC and compared it with the widely applied Canadian Fire Weather Index (FWI). We found that in addition to stand characteristics, ground layer FMC was highly dependent on the species so that Dicranum was the moistest, and Cladonia the driest. In the humus layer, the differences among species were small. Overall, the FWI was a slightly better predictor of FMC than the FFI. While the FFI generally predicted ground layer FMC well, the shape of the relationship varied among the four species. The use of auxiliary variables thus has potential in improving predictions of ignitions and forest fire risk. Knowledge of FMC variation could also benefit planning and timing of prescribed burns.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".