Potential of near-infrared spectroscopy to characterize wood products<sup>1</sup>This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.
Bibliographic record
Abstract
Near infrared spectroscopy (NIRS) has high potential as a rapid nondestructive approach to identifying wood species and estimating properties that affect their utilization. This study found that NIRS could differentiate certain wood species groups. True firs (balsam fir ( Abies balsamea (L.) Mill.) and subalpine fir ( Abies lasiocarpa (Hook.) Nutt.)) could be distinguished from pine and spruce in eastern and western spruce–pine–fir, respectively, more than 95% of the time. Western hemlock ( Tsuga heterophylla (Raf.) Sarg.) could be differentiated from amabilis fir ( Abies amabilis Douglas ex J. Forbes) in the Hem–Fir species group with about 90% accuracy. Average wood moisture content (MC) of air-dried southern pine and western redcedar ( Thuja plicata Donn ex D. Don) samples wood could be estimated by NIRS ±10%–30% at high moisture contents and more accurately (±2%–5%) below 30% MC. Conditioned samples of amabilis fir had predicted MCs within 2%–3% of measured values in the 0%–30% MC range. However, the broad applicability and response of NIRS to a number of factors may be its greatest weakness, since measurements for a specific response, such as MC or species differentiation, may be confounded by the effects of other variables, such as surface roughness and localized density differences. It is recommended that instrumentation with a relatively large probe (large illumination area) be used to average such variables in the sample.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".