Chemical Composition of Wood Chips and Wood Pellets
Bibliographic record
Abstract
The chemical composition of 23 wood chip samples and 132 wood pellet samples manufactured in the United States and Canada were analyzed for their energy and chemical properties and compared to German standards for pellet quality. The pellet samples obtained from various locations across northern New York and New England included 100 different manufacturers and duplicate samples of some brands. The calorific value, moisture content, and ash content of the samples were determined according to the American Society for Testing and Materials (ASTM) methods. Sulfate and chloride samples were prepared using ASTM methods and analyzed by ion chromatography (IC). The elemental compositions of the ashed wood samples were determined using inductively coupled plasma mass spectrometry (ICP–MS). Mercury was measured by direct analysis of wood samples. The distributions of the sample characteristics, such as heating value, ash content, moisture content, ions, and heavy elements, are presented. Major ash-forming elements were Ca, K, Al, Mg, and Fe. Although heavy elements are found naturally in wood and bark, some pellet samples had unusually high concentrations of heavy elements. This contamination was likely because of inclusion of extraneous materials, such as scrap or painted wood, bark or leaves, and other possible contaminants, during pellet manufacturing processes. Most of the commercially available wood pellets of this study would meet German and European industrial standards. However, standards for elemental compositions of commercial wood pellets and chips need to be established in the United States to exclude extraneous materials. The promulgation of such standards would reduce environmental problems related to air emissions and ash used as fertilizers for agriculture soils, where there are limits on the allowable concentrations for many elements.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".