Wood classification study based on thermal physical parameters with intelligent method of artificial neural networks
Bibliographic record
Abstract
In this study, 65 kinds of wood samples were classified by using artificial neural networks based on the measured value of wood thermal physical parameters. First, the thermal conductivities and the thermal diffusion coefficients of the wood samples were measured. The transient temperature rise curve of wood samples during the test process was recorded, and the characteristic values of the transient temperature rise curve were extracted by logarithmic curve fitting. The emissivity spectrum representing the thermal physical properties of wood surface was measured, and the characteristic spectral data were selected according to the principal component analysis. An artificial neural network model was established based on the extracted feature values and characteristic spectral data to classify the wood species. The experimental results showed that the comprehensive correct classification rate of the proposed wood classification method was 99.85%. In addition, the proposed wood classification method was compared with a wood classification method based on laser induced breakdown spectrum and near infrared spectrum, which indicates the feasibility of wood classification based on the values of wood thermal physical properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".