A single point NMR method for an instantaneous determination of the moisture content of wood
Bibliographic record
Abstract
Abstract Nuclear magnetic resonance (NMR) enables an instantaneous determination of the proton density in liquids and is thus convenient for determining the moisture content (MC) of wood. We demonstrated that the MC of a wood sample can be determined instantaneously on the basis of its mass (m) and the amplitude of its NMR free-induction-decay (FID) signal. The measurement is based on the assumption that the only liquid in the wood is water and that the relationship between the amplitude of the FID signal (S) and the mass of the water (m w ) in the sample is linear, i.e., S=k m w +k′ (m-m w ), and can be precisely calibrated for a given NMR probe and NMR spectrometer setup (in our case k=105 AU g-1 and k/k′ =34). With the FID signal converted into the mass of water, the MC is calculated as: MC=(S-m k′)/(m k-S). After the initial calibration of the FID signal with respect to the content of water, the correctness of the method was verified on samples of different wood species with various MCs. The results confirmed that the proposed method is comparable in terms of accuracy and reliability to the gravimetric method, regardless of the species of wood. As the method is instantaneous, it might become the method of choice in applications where a short measurement time combined with a high accuracy is demanded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".