Near-Infrared Spectroscopic Separation of Green Chain Sub-Alpine Fir Lumber from a Spruce-Pine-Fir Mix
Bibliographic record
Abstract
The intention of this exploratory study was to determine whether near-infrared spectroscopy, combined with multivariate statistical modeling, could become a swift and accurate tool for identifying sub-alpine fir within a typical spruce-pine-fir (SPF) lumber mix in the green chain of a sawmill. This need arises from the difficulty encountered in the drying sub-alpine fir. Its identification and removal from the SPF mix before kiln drying may be quite beneficial for producing high quality lumber. Near-infrared spectra were obtained from scanning of small specimens that were prepared from freshly cut trees. The results of the initial principal component analysis indicated that all four components could be used for species differentiation with the help of partial least squares discriminant analysis. All specimens in the training set were fitted into the correct sub-group of either fir or spruce-pine groups. The test set was validated and it revealed that all specimens were correctly classified. The outcome also confirmed that near-infrared spectroscopy combined with multivariate statistical modeling could be a suitable prediction model for separation of sub-alpine fir from the SPF mix.
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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 | 0.000 |
| 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 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".