Development of near infrared reflectance analysis calibrations for estimating genetic parameters for cellulose content in <i>Eucalyptus globulus</i>
Why this work is in the frame
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Bibliographic record
Abstract
Determining kraft pulp yield in the traditional way is slow and expensive, limiting the numbers of samples that may be processed. An alternative is to use a secondary standard, such as cellulose content of the wood, which is strongly correlated with kraft pulp yield. The feasibility and efficiency of predicting cellulose content using near infrared reflectance (NIR) analysis was examined for Eucalyptus globulus Labill. Calibrations for NIR prediction of cellulose content indicated that NIR analysis could be used as a reliable predictor. Standard errors of calibration were 1% or lower, and there was excellent agreement between laboratory and predicted cellulose values. Cellulose content was under moderate genetic control (h 2 ranging from 0.32 to 0.57), and genetic correlations with tree diameter and basic density were variable (ranging from 0.11 to 0.51 and 0.33 to 0.67, respectively). The advantages, disadvantages, and potential applications of NIR analysis for predicting cellulose content are examined.
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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 | 0.000 | 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 | 0.000 | 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 it