Wood classification study based on thermal physical parameters with intelligent method of artificial neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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