Drought models for the study of the vegetation moisture content; analysis of performance of different models in two mediterranean ecosystems for application in forest fire prevention
Bibliographic record
Abstract
Abstract Vegetation water content is one of the most important parameters of vegetation status and health, and consequently a natural element that regulates several ecosystems worldwide; moreover, considering vegetation as fuel, this variable is related to wildfires. Forest fires and vegetation resistance to ignition during periods of drought are both strictly related to climate characteristics of the area. The contribution of this work is to evaluate the performance of vegetation drought models using field measured data (data related to local adaptation and phenotypic plasticity), data usually lacking. In the present work, moisture content of shrub vegetation and live foliage (fine fuels) were detected by field measurements of in Tuscany (Italy). In two plots of Quercus ilex L. and mixed broadleaves forest, seasonal and inter-annual variations of live fine fuels of several species are analyzed. The selected species constitute two sets (shrubs and trees) of vegetation typology characterized by a representative seasonal variability in mediterranean ecosystems. From nearby stations meteorological data were collected in each study area for the evaluation of fuel moisture indicators, including the Drought Code (DC) used in the Canadian Forest Fire Danger Rating System. The results of the present work have shown that for the summer season the slow response of live fine fuel moisture content (LFMC) to meteorological conditions (namely to precipitation), was well described by the DC. Empirical correlations between LFMC and DC for each species and site are proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".