Fire-severity mitigation by prescribed burning assessed from fire-treatment encounters in maritime pine stands
Bibliographic record
Abstract
Maritime pine (Pinus pinaster Ait.) stands are prone to high-intensity fire. Fuel treatments lessen potential fire behaviour and severity, but evidence of their effectiveness when tested by wildfire is extremely scarce in Europe. We assess the longevity of prescribed burning in maritime pine plantations in decreasing fire severity. Heights of crown scorch and stem-bark char were measured in treated and untreated adjacent areas after fire-treatment encounters in Portugal, Italy, and Australia. Treatment effect was quantified as the log-transformed ratio between prescribed-burned and untreated fire-severity data. Linear mixed modelling indicated that for typical wildfire conditions, the effect of prescribed burning in crown scorch height lasts 2–6 years. The persistence of prescribed burning benefits is higher for fire control operations than for fire-severity mitigation. Regression tree analysis of data from one wildfire highlighted the roles of wind direction, topography, and stand height in explaining variability in fire severity. A 4-year interval between prescribed burning treatments in maritime pine stands is recommended in general, depending on site quality and stand age and structure. Improved fuel-consumption prescriptions and monitoring procedures are advisable to foster prescribed-burning effectiveness and its evaluation.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".