Near-infrared spectroscopy as a potential quality assurance tool for the wood preservation industry
Bibliographic record
Abstract
Preservatives are used to extend the service life of wood used in situations where it is vulnerable to biodegradation. Wood preservation standards typically specify quality assurance procedures to differentiate heartwood and sapwood and to measure preservative loading and penetration. For spruce and hem–fir there are no reliable chemical spot tests to differentiate sapwood and heartwood. This makes it more difficult for these species to meet penetration requirements. For many of the recently introduced carbon-based preservatives, the only methods available to determine penetration involve detecting a surrogate in the formulation, or only some of the active ingredients. Multivariate models based on near-infrared spectroscopy (NIRS) have been used to predict a wide range of wood properties over the past 20 years. The present research evaluates the potential use of NIRS-based models as quality assurance tools for the wood preservation industry. Models were developed to differentiate hemlock and amabilis fir sapwood and heartwood. NIRS-based models were also able to differentiate untreated wood from wood treated with DDACarbonate and wood treated with tebuconazole. Models developed to predict DDACarbonate and tebuconazole retention were moderately accurate, but likely not precise enough to replace current quantitative assays.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".