Predictions of wood density and module of elasticity of balsam fir (<i>Abies balsamea</i>) and black spruce (<i>Picea mariana</i>) from near infrared spectral analyses
Bibliographic record
Abstract
The predictions of properties for wood disc average are seldom reported, and they are important for sorting out logs based on their quality. The minimum near infrared (NIR) spectra required to predict wood disc average properties would also be of critical importance. In this study, calibration and prediction models for wood disc average properties were developed using NIR spectral data for balsam fir (Abies balsamea (L.) Mill.) and black spruce (Picea mariana (Mill.) B.S.P.) samples collected from 14 different sites across Newfoundland, Canada. The calibration was done against area-weighted average wood properties determined by SilviScan. NIR spectra were collected in 18 mm increments from the radial–longitudinal face of green and oven-dried samples. Results showed that using NIR spectra from three spots per wood strip was sufficient for the modeling and prediction for density and module of elasticity (MOE). The coefficients of determination ranged from 0.76 (MOE of green wood samples) to 0.88 (density of oven-dried wood samples). However, the microfibril angle (MFA) cannot be well predicted from either green wood or oven-dried wood NIR spectra. Our results further showed that the NIR spectra collected from oven-dried wood samples gave better calibration and prediction than those collected from green wood samples.
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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 | 0.000 | 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 | 0.001 |
| 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 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".