Rapid prediction of shrinkage and fibre saturation point on teak (
Bibliographic record
Abstract
• Dimensional stability, along with the natural durability and colour of the wood, is one of the most important characteristics of teak used as timber. However, it is very time-consuming to take measurements of this kind. For the purposes of selection for the production of improved varieties, the number of samples to be measured rapidly exceeds the capacity of a traditional laboratory. • Near-infrared spectroscopy, based on a set of reference data, is a tool enabling many of the chemical properties of wood to be predicted and the number of laboratory measurements to be reduced exponentially. The issue here is a question of checking the effectiveness of NIRS tool to build models and predict the shrinkage and fibre saturation point of teak wood from Togo. • The results show the possible use of NIRS to measure the dimensional stability of teak wood and that it is appropriate to choose the type of wood and type of surface to be measured by NIRS. The best prediction models for radial and tangential shrinkage and fibre saturation point give R2 values of 0.72, 0.83 and 0.87 respectively with ratios of performance deviation of 1.8, 2.4 and 2.8. • Consequently, after verification on other sets of teak samples, which may or may not be included in the prediction model, NIRS can be used to predict shrinkage and fibre saturation point values accurately for a large number of samples, making it possible to include these characteristics in the selection criteria for classifying wood and high throughput phenotyping.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".