Effect of moisture content and canopy height on flammability of gorse (Ulex europaeus) shrubs measured in a large-scale oxygen consumption calorimeter
Bibliographic record
Abstract
Background Vegetation flammability is strongly influenced by fuel load, structure and moisture content (MC). Previous studies often tested individual plants or plant parts, limiting their relevance to landscape-scale fire modelling. Aims This study aimed to assess how MC and canopy height affect gorse shrub flammability using a new area-based approach. Methods Combustibility, sustainability and consumability of 36 gorse shrubs of three heights (0.9, 1.3 and 1.7 m) and two MC conditions (dried and fresh), each representing 2 m2 of gorse canopy, were tested in a large-scale oxygen consumption calorimeter using a novel sample preparation protocol and a custom 0.91 MW ignition system. Key results The reduction in energy release between fresh and dried shrubs aligned with the energy absorbed by water contained in fine particles (0–5 mm). Most flammability metrics were affected by shrub height and MC, although heat release per unit of consumed dry mass did not change with MC. Conclusions Water in fine fuels limits total heat release by reducing the combustion of larger particles. Taller gorse canopies generate more heat owing to higher fuel loads. Implications The proposed area-based flammability testing approach supports integration with remote sensing data, enabling improved physics-based fire behaviour models and risk assessments in shrubland ecosystems.
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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 | 0.000 | 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".