Analyzing the Distribution of Moisture Content in Kiln-dried Lumber Using Goodness of Fit Techniques – with Procedures
Bibliographic record
Abstract
Knowledge of the distribution of moisture content in kiln-dried lumber is essential for quality control work. This paper describes formal methods and procedures for determining the distribution of moisture content in kiln-dried lumber using goodness-of-fit analysis, for three known distributions: Normal, Lognormal, and Weibull. Two graphical methods are employed: investigation of symmetry, and probability plotting. Formal mathematical tests are carried out for each distribution using test statistics based on the empirical distribution function (ECDF). The methods are tested on Douglas-Fir (Pseudotsuga menziesii) lumber collected from a production facility in British Columbia, Canada. Test results show that moisture content of kilndried lumber is well modeled by the threeparameter Lognormal distribution. In order to properly test the distribution of moisture content, all three parameters of the Lognormal distribution need to be considered: 'scale', 'shape' and 'threshold'. For kiln-dried lumber, the left bound (or threshold) of the distribution is greater than zero percent, and slightly smaller than the minimum lumber MC.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".