Goodness-of-Fit for Mechanical Properties Distribution of Larch
Bibliographic record
Abstract
Six different probability distributions, Johnson's S B , 2p-lognormal, 3p-lognormal, normal, 2p-Weibull, and 3p-Weibull, were used for testing their relative goodness of fit in describing modulus of rupture (MOR), modulus of elasticity (MOE), ultimate tension strength (UTS), and ultimate compression strength (UCS) of larch (Larix gmelini) dimension lumber.The populations of lumber consisted of 80 data sets with different mechanical properties, sizes, and structural grades.The Kolmogorov-Smirnov test was selected to be the goodness-of-fit criteria in this study.The 5-and 50-percentile values of these four different mechanical properties of larch lumber were estimated using both the inverse function of various distribution functions and the nonparametric method.Results indicated that 3p-lognormal was the optimal function in describing MOE of larch lumber.The 5-and 50-percentile estimations using the inverse function of 3p-lognormal were the closest values derived through the nonparametric method.Johnson's S B was the best one in describing MOR, UTS, and UCS.The 5-and 50-percentile estimations using the inverse function of Johnson's S B were the closest values derived with the nonparametric method.The distributions of these four mechanical properties of larch lumber were independent of the structural grade and size.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".