The effects of storage on the net calorific value of wood pellets.
Bibliographic record
Abstract
The wood pellet export from Canada to Europe has been increasing steadily in recent years (roughly 1.8 million ton in 2013). Due to distances involved, wood pellets remain in transit and storage for months before their final consumption. The net calorific value determines the price of wood pellet purchase in Europe. There have been concerns about the changes of net calorific values over time. In this study, the effects of storage time, storage configuration, storage temperature, and wood pellet quality on the net calorific value of wood pellets for a period of 6 months were investigated. Storage configurations were open or closed and storage temperatures were 25 °C, 35 °C and 45 °C. Two types of wood pellets used were whitewood and mixed. The results in closed storage indicated that storage time had a positive effect on the net calorific value where the net calorific value increased by 1% to 2% over the storage period. In open storage, the moisture content had the most significant impact on the net calorific value. The net calorific values of the two types of wood pellets were found to be significantly different at p < 0.001. A multivariable linear regression and analyses of variance performed verified the graphical results. Lastly, the authors postulated that the higher energy potential compounds, such as aldehyde and ketone, produced during pellet storage, caused the increase in net calorific values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".