Use of weather generators for assessing local scale impact of climate change on dead fuel moisture
Bibliographic record
Abstract
The main aims of this work are to identify useful tools to determine potential impacts of expected climate change on dead fuel status in Mediterranean shrubland and, in particular, to estimate the effect of climate changes on the number of days characterized by critical values of dead fuel moisture.Measurements of dead fuel moisture content in Mediterranean shrubland were performed in North Western Sardinia (Italy) for six years by using humidity sensors.Meteorological variables were also recorded.Data were used to determine the accuracy of the Canadian Fine Fuel Moisture Code (FFM code) in modelling moisture dynamics of dead fuel in Mediterranean vegetation.Critical threshold values of FFM code for Mediterranean climate were identified by percentile analysis, and new fuel moisture code classes were also defined.A stochastic weather generator (M&Rfi), linked to climate change scenarios derived from 17 available General Circulation Models (GCMs), was used to produce synthetic weather series, representing present and future climates, for the selected site located in North Western Sardinia, Italy.The number of days with critical FFM code values for present and future climate were calculated and the potential impact of future climate change was analysed.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".