Bibliographic record
Abstract
The heat of combustion of burning trees is often used in forest-fire hazard modeling to relate mass-loss results to the heat produced; therefore reliable values are needed. Experimental results for the effective heat of combustion of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees are presented as a function of moisture content. It is also shown that during a forest fire, the effective heat of combustion does not correspond to the oxygen-bomb-test value. Instead, the value will always be lower, since the heat of combustion of char is higher than that of the pyrolysate vapors, and char mostly remains unburned during a forest fire. These are the first and only experimental results obtained from testing of actual trees. But results from benchmark testing and studies on wood products by other investigators are broadly consistent with our findings. It is further shown that moisture content has a major effect on the effective heat of combustion. A quantitative expression for the effective heat of combustion, as a function of moisture content, is obtained. Benchmark testing by earlier researchers established that generally there is only a slight species effect on the heat of combustion; therefore the present Douglas-fir results can be applied in more general forest-fire modeling.
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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 | 0.000 | 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) | 0.003 | 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".