Radial variation in Kraft pulp yield and cellulose content in <i>Eucalyptus globulus</i> wood across three contrasting sites predicted by near infrared spectroscopy
Bibliographic record
Abstract
There is little published information on the variability of pulp yield within eucalypt stems. This is primarily due to the difficulty in measuring pulp yield in small samples, especially those obtained nondestructively. Thus, information of the effect of site, climate, and silviculture on pulp yield variability is lacking, minimizing its consideration in the economic optimization of pulpwood production. Near infrared spectroscopic measurement of pulp yield directly from intact wood surfaces at a small, radial sampling interval provides forest managers with opportunities to better understand and manage its sources of variability. Calibrations based on solid wood samples were constructed to describe radial variation in Kraft pulp yield and cellulose content using intact wood samples from Eucalyptus globulus Labill. Plantations at three sites with contrasting annual rainfall were studied. Pulp yield and cellulose content were higher at the more productive, wetter sites. Outer wood near the cambium had pulp yield values up to 8% higher than those at the pith. There was no clear intra-annual cycle of variation. The more productive sites exhibited significantly steeper pith-to-bark increases in pulp yield than the driest site. This has implications for optimizing pulp productivity by considering rotation length and site quality.
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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 | 0.000 | 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 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".