A NIR machine for moisture content measurements of forest biomass in frozen and unfrozen conditions
Bibliographic record
Abstract
Moisture content (M) is an important quality parameter of wood chips, strongly influencing the net calorific value as received. The current standard for determining M, the oven-drying method, is slow and sometimes the sampled lot is combusted before the determination is concluded. This increases the risk of inefficient combustion and reduces the value of M determination. In Scandinavia, winter biomass supply operations are the major source of forest biomass chips to the heating plant and frozen chips are commonly delivered. Comparisons were made between the Prediktor Spektron Biomass, which measures M by near-infrared (NIR) spectroscopy, and the oven-drying method. M measurements were carried out for a total of four biomass materials in both frozen and unfrozen condition, where M ranged from 24% to 65% wet basis. On average the machine underestimated M by 0.34%-units for frozen materials and overestimated M by 0.68%-units for unfrozen materials. The results for repeatability of measurements showed that 95% of the measurements were within ±2.24%-units of the mean for the frozen materials and within ±1.72%-units for the unfrozen. This shows that the machine was suited to measure unfrozen and frozen material, and allows the measurement of bulky samples and isn’t constrained by particle size.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".