Statistical Technique for Grouping Tropical Timbers into Similar Strength Groups
Bibliographic record
Abstract
This paper focuses on statistical technique for the assessment of grouping tropical timbers into similar strength groups. The Student’s t-test was conducted to evaluate whether the values of mean MOE in bending of two timbers are statistically different from each other. A total of 23 Malaysian hardwoods of different strength classes were assessed by comparing MOE value of one timber to the next. The assessment is limited to species of data obtained from at least 40 specimens and from at least 5 trees. However, more species can be added by conducting extra test based on the similar test procedure. The results showed that from 23 timbers evaluated, they fall into 6 different levels, indicating 6 different timber groups of similar MOE. The outcome is almost similar to the previous groupings done through different methods. The grouping will serves as a guideline for converting small size specimens’ data into the equivalent structural timber test values.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".