Genetic improvement of kraft pulp yield in <i>Eucalyptus nitens</i> using cellulose content determined by near infrared spectroscopy
Bibliographic record
Abstract
Eucalyptus nitens (Deane & Maiden) Maiden (shining gum) is widely grown for kraft pulp production in many cool temperate regions of the world. Improving the kraft pulp yield of this species is important for increasing plantation profitability, but traditional assessment is slow and expensive. Cellulose content, which is strongly correlated with pulp yield, has been used as an alternative in tree breeding programs. However, a direct measure of cellulose content still relies on wet chemistry, limiting the number of samples that can be processed and the subsequent gains that can be made in a tree breeding program. An indirect method such as near infrared (NIR) spectroscopy provides a large increase in the numbers of samples that can be analysed. In this study, the genetic gains in cellulose content of E. nitens were compared using cellulose content, determined using wet chemistry and predicted by NIR calibrations based on different sampling intensities. Genetic gains based on NIR-predicted cellulose content were high, and a large proportion of the gain was achievable using a direct measure of cellulose. Calibrations were robust and generally could be reliably used across sites. NIR-predicted cellulose is highly heritable, with heritabilities comparable to or better than direct measures of cellulose.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".