Using near-infrared hyperspectral images on subalpine fir board. Part 1: Moisture content estimation
Why this work is in the frame
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Bibliographic record
Abstract
AbstractIn this study, moisture content (MC) images of subalpine fir (abies lasiocarpa Hook) boards were derived from near-infrared hyperspectral images in the 947–1637 nm range. One hundred and seven cubic samples with the size of 4 cm were prepared from 14 boards. All samples were dried to various MCs during several steps until being completely dried. Hyperspectral images and weight measurements were acquired over each sample at each drying step. The samples have MC ranging from 1% to 137% (dry basis). The images were first calibrated into reflectance. Then, bad pixels were found and replaced by a corrected value using a median filter. A modified version of the boxplot method was used to find abnormal spectra that were then removed. The remaining spectra were converted into absorbance spectra. They were then split into a calibration and a validation data-set according to the boards they were extracted from to build and validate a partial least squares (PLS) regression model between the near-infrared absorbance spectra and the measured MCs. The PLS model was applied first to the sample images, then to the whole board images in order to produce 2D images of MC.Keywords: Hyperspectral imagingboarddistribution of moisture contentnear-infraredPLSsubalpine fir AcknowledgmentsThe authors thank G. Chow from FPInnovations as well as K. Phung (UNB) for their help during the experiments. The study was supported by an NSERC Strategic Grant awarded and a New Brunswick Foundation for Innovation grant to B. Leblon.
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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 it