Allometric equations for estimating canopy fuel load and distribution of pole-size maritime pine trees in five Iberian provenances
Bibliographic record
Abstract
Adequate quantification of canopy fuel load and canopy bulk density is required for assessment of the susceptibility of forest stands to crown fire and evaluation of silvicultural treatments aimed at reducing the risk of crowning. The use of tree biomass equations and vertical profile distributions of crown fuels provide the most accurate estimates of the canopy fuel characteristics. In this study, 100 pole-size maritime pine (Pinus pinaster Aiton) trees were destructively sampled in five different sites, covering a wide range of its geographical distribution in the Iberian Peninsula. To estimate crown fuel mass, allometric equations were fitted separately for needles, twigs, and fuel available for crown fire. Models were also fitted to characterize the vertical fuel distributions as a function of tree height. All models were fitted simultaneously to guarantee additivity among tree biomass components, and corrections were also made for heterocedasticity and autocorrelation. Diameter at breast height was the best explanatory variable for all the allometric models. The vertical distribution of crown biomass fractions along tree height depended on the crown size and tree dominance. The system of equations provides a good balance between accurate predictions and low data requirements, allowing quantification of canopy fuel characteristics at stand level.
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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 | 0.002 |
| 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".