Investigating the Differences between the Self-heating of Bark and Wood Piles during Storage through the Use of Computer Modeling
Bibliographic record
Abstract
Due to the problems associated with self-heating in large piles of woody materials and the requirement of Nova Scotia Power to use bark to power a large biomass boiler, modeling was conducted to determine whether wood self-heating models could be used for softwood bark piles. During an approximately 100 day storage trial of 2 large bark dominated biomass piles, the piles peaked at average temperatures of 40째 C and 50째 C. Modeling in Comsol Multiphysics with bark parameters obtained from physical characterization tests of material from trial site yielded accurate predictions of pile temperature, with pile 2 being simulated closely by the model. Pile heating differed between sections of the piles, with the bottoms of the pile heating much slower than the rest. A sensitivity analysis yielded several parameters, which affected the model, such as bulk density, thermal conductivity, pile height and microbial death and growth rates.
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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".