Estimation of whole-tree kraft pulp yield of <i>Eucalyptus nitens</i> using near-infrared spectra collected from increment cores
Bibliographic record
Abstract
Eucalyptus nitens (Deane and Maiden) Maiden (shining gum) is widely grown for kraft pulp production. Improving the kraft pulp yield of E. nitens increases plantation profitability but traditional assessment is slow and expensive, which hinders improvement. Near-infrared (NIR) spectroscopy provides a rapid and inexpensive method for estimating pulp yield, but studies have been limited to estimating whole-tree pulp yield using whole-tree composite samples obtained destructively. For whole-tree pulp-yield calibrations to be used non-destructively they must be applied to increment cores. In this study we used a Tasmanian E. nitens whole-tree pulp yield calibration to estimate the whole-tree pulp yields of trees from a site not included in the calibration. This was done using NIR spectra from increment cores and whole-tree composite chips. Predictions of whole-tree pulp yield based on increment cores were better than those obtained using whole-tree composite chips. The accuracy of pulp-yield predictions was greatly improved by adding a small number of prediction-set samples to the calibration sets. Calibrations for estimating whole-tree pulp yield were also obtained using NIR spectra from milled cores and whole-tree composite chips. The calibrations had similar statistics, indicating that it is possible to obtain calibrations for estimating whole-tree pulp yield based on increment-core NIR spectra.
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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 | 0.000 |
| 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".