Chemical and Carbon‐13 Cross‐Polarization Magic‐Angle Spinning Nuclear Magnetic Resonance Characterization of Logyard Fines from British Columbia
Bibliographic record
Abstract
Phasing out beehive burners and rising costs for landfilling have increased the need to widen options for utilization of the smaller size fractions of woody wastes generated during log handling and sawmilling in British Columbia. We characterized several size classes of logyard fines up to 16 mm sampled from coastal and interior operations. Total C, total N, ash, and condensed tannin concentrations were consistent with properties derived largely from wood, with varying proportions of bark and mixing with mineral soil. Especially for < 3-mm fractions, the latter resulted in high ash contents that would make them unsuitable for fuel. Solid-state 13C cross-polarization magic-angle spinning (CPMAS) nuclear magnetic resonance (NMR) spectra were consistent with the chemical data, with high O-alkyl intensity and similarity to naturally occurring woody forest floor; no samples were high in aromatic or phenolic C. Aqueous extracts of two < 16-mm fines, which accounted for only a small proportion of the total C, were enriched in alkyl C and had low or undetectable tannins. Application to forest sites might cause short-term immobilization of N, but also might include possible longer-term benefits from reduction of N loss after harvesting and restoration of soil organic matter in degraded sites.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".