Quantitative magnetic resonance measurements of low moisture content wood<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
Magnetic resonance spectroscopy and imaging are well established analytical tools with ever-increasing ranges of application. They are, however, generally underutilized in the areas of forestry and wood science. This is in part due to the complex nature of wood and wood–water interactions and also to the need of wood scientists for quantitative measurements of moisture content, fluid flow, wood structure, etc. Furthermore, magnetic resonance instruments have historically been large, sophisticated, and expensive and not generally compatible with wood production facilities. In this paper, we discuss the limitations of magnetic resonance to applications such as wood and describe how, with recent developments in magnetic resonance imaging technology, these limitations can be overcome. We highlight our own work with quantitative moisture content measurements and outline progress in the development of simpler, lightweight, and mobile magnetic resonance instruments. These are promising devices for routine portable magnetic resonance spectroscopy and imaging, with the potential to finally extend these powerful techniques to the world of foresters and wood scientists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".