A NIR machine for moisture content measurements of forest biomass in frozen and unfrozen conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it