Using near-infrared hyperspectral images on subalpine fir board. Part 2: Density and basic specific gravity estimation
Bibliographic record
Abstract
Wood density (ρMC) and basic specific gravity (BSG) are important properties in several forest products manufacturing processes. In this study, near-infrared hyperspectral images were tested to produce two-dimensional (2D) ρMC and BSG images of subalpine fir (Abies lasiocarpa Hook) board. A total of 107 cubic samples with the size of 4 cm were prepared from 14 boards. All samples were dried to various moisture contents (MCs) during several steps until being completely dried. The resulting MCs ranged from 1% to 137% (dry basis). After the last drying step, the samples were soaked in water to determine BSG. Hyperspectral images and weight measurements were acquired over each sample at each drying step. ρMC was also estimated at each MC level. Partial least squares (PLS) models were developed for estimating both ρMC and BSG from the near-infrared hyperspectral imaging (NIR-HSI) system absorbance spectra acquired over all the samples during each drying step. The ρMC model provides a reasonable accuracy with the validation data-set (R2 = 0.81, RMSE = 39 kg/m3, and RPD = 2.3). For BSG, only models built with samples having MC of less than 12% are significant. The calibration data-set provides similar accuracy as the ρMC model (RMSE = 0.004, R2 = 0.82, and RPD = 2.28), but the accuracy is lower with the validation data-set (RMSE = 0.007, R2 = 0.53, and RPD = 1.39). Our data-set has BSG values varying only from 0.326 to 0.374, and further work is needed to apply these methods to a data-set that includes a more extended range of BSG variations for improving estimation accuracy.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".