Effects of temperature and moisture content of logs on size distribution of black spruce chips produced by a chipper-canter at two cutting widths
Bibliographic record
Abstract
Four matched groups of black spruce logs were processed with a chipper-canter at temperatures of 20, 0, -10, and -20 °C. Each log was transformed at two moisture contents (MC, green and air-dried) using two cutting widths (CW, 12.7 and 25.4 mm). Mean MC for each CW was assessed from a sample of the obtained chips. Knot characteristics were measured on the cant surfaces after log processing. Chip size was assessed by thickness (Domtar classifier) and width/length (Williams classifier). The results showed that the chip size was significantly affected by the CW and temperature, and in a lesser degree by the chip MC. The weighted mean chip thickness (WCT) increased with the CW. As temperature decreased below 0 °C, WCT and accepts decreased, while proportions of fines and pin chips increased. Chips obtained from green logs were thinner compared to air-dried logs when processed at the coldest temperature (minus 20 °C). The number and size of knots had an important impact on chip size, particularly on WCT. Multiple regressions were developed to predict WCT. Results showed the potential benefits of measuring log temperature and knot features to reduce chip thickness variation during fragmentation and thus improving chip size uniformity.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".