Assessment of biomass functions for calculating bark proportions and ash contents of refined biomass fuels derived from major boreal tree species
Bibliographic record
Abstract
Knowledge of the components of above-ground biomass of low-quality stems harvested to produce biofuel pellets is important, since bark has higher ash contents (a key quality parameter for the pellets) than wood. Therefore, single-tree biomass functions by Marklund (1988. Rep. 45. Department of Forest Survey, Swedish University of Agricultural Sciences, Umea, Sweden.) were evaluated using a sample population of 1612 Scots pine ( Pinus sylvestris L.), Norway spruce ( Pices abies (L.) Karst.), and birch ( Betula pendula Roth and Betula pubescens Ehrh.) trees and three methods to model bark proportions. Bark percentages calculated subtractively using functions for stem biomass over and under bark showed anomalous patterns, especially for pine and birch. However, additive use of biomass functions for bark biomass and stem biomass under bark resulted in similar patterns to bark percentages traditionally calculated from bark biomass and stem biomass over bark. Published ash contents of bark and stemwood indicate that pine, birch, and spruce stems with breast height (1.3 m) diameters of 4–40, 4–30, and >19 cm, respectively, have <0.7% ash contents (the current limit for the highest quality fuel pellets). However, if the highest recorded ash contents are used, only pine stems meet this criterion. Thus, material of different species and stem dimensions may need to be carefully mixed when whole-stem biomass is used as feedstock for pelletizing.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".