Variation of White Spruce Carbon Content with Age, Height, Social Classes and Silvicultural Management
Bibliographic record
Abstract
The accuracy and precision with which carbon amounts have been accounted for in forests have been questioned. As countries seek to comply with agreements to reduce global warming and industries seek to maximize bioenergy potential, this matter has increased international concern. White spruce (Picea glauca (Moench) Voss) stand density management trials in the Petawawa Research Forest, Ontario, Canada, were sampled to evaluate carbon concentration variation within trees and plots of differing stand density. Sample-drying methodologies were also tested to compare freeze-dried carbon (FDC) and oven-dried carbon (ODC) measurements. The average FDC was 51.80 ± 1.19%, and the corrected freeze-dried carbon content (FDCCOR) was 51.76 ± 1.33%. The average ODC was 49.10 ± 0.92%, and the average volatile carbon fraction (Cvol) was 2.67 ± 1.71%. FDC was higher than ODC (mean of the differences = 2.52) and generally more variable. ODC significantly decreased radially and longitudinally. FDC was significantly affected by thinning, where heavy treatments resulted in the highest FDC amounts compared to medium, light, and control treatments. In addition to reducing carbon content (CC), drying influences wood CC in many ways that are still to be elucidated. The results of this study suggest that ODC should continue to be used within the bioenergy industry, while FDC must become the preferred standard for carbon accounting protocols.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".