Lognormal control charts for moisture content of kiln-dried lumber
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
This paper focuses on the application of statistical process control principles to monitor the lumber kilndrying process through the use of innovative quality control charts.Three Lognormal control charts are proposed to monitor quality characteristics that follow a three-parameter Lognormal statistical distribution.The first two control charts, called the "scale chart" and the "chart for geometric means," monitor the central tendency of the process.The third chart, called the "shape chart," monitors the process variability.Practical procedures are presented for calculating center lines and control limits, and for plotting the data on the charts.A rationale is given for using geometric means rather than arithmetic means for assessing process' central tendency.The choice of parameters to be monitored on control charts, along with parameter estimation issues, are discussed.A succinct comparison with the customary "Normal" charts is also included.The methods presented were tested on a data set of Douglas-fir (Pseudotsuga menziesii) lumber collected from a production facility in British Columbia, Canada, for which the statistical distribution of moisture content measurements was determined to be well modeled by a three-parameter Lognormal distribution.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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