Physicochemical characteristics controlling the flammability of live Pinus banksiana needles in central Alberta, Canada
Bibliographic record
Abstract
Background Few studies have focused on the integral assessment of live fuel flammability in the boreal forest. Aims We aimed to examine the flammability of living needles of jack pine (Pinus banksiana) as characterised by their form, moisture and chemical content at different ages and times of the year. Methods With a calorimeter and open flame, we estimated needle ignitability, consumption rate, maximum speed and amount of energy released. We measured their form, moisture and chemical content. Key results Needle form has a major effect on ignitability, whereas chemical composition primarily influences the amount and rate of energy release. Needles <1 year old are the least flammable; they are rounder and voluminous, with higher moisture and nitrogen content. Needles ≥1 year old are more flammable; they are drier, more curved, thinner, longer, and contain more carbon and terpenes. Needles release more energy during the early growing season, when starch and lipids are at their peak concentrations. Conclusions Moisture content is not the major factor affecting the flammability of jack pine live needles; physicochemical changes specific to age and month of collection are the most influential factors. Implications Assessing the multi-faceted properties of live fuels flammability will help to comprehend stand- and landscape-scale fire behaviour.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".