Profile Charts for Monitoring Lumber Manufacturing Using Laser Range Sensor Data
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
Real-time technologies using noncontact laser range sensors (LRS) have recently been introduced to improve statistical process control (SPC) programs in automated lumber mills by greatly increasing the volume of data available for SPC. However, present SPC procedures based on sampling theory developed for manual data collection do not fully utilize data from these systems. A new system of control charts is introduced here that simultaneously monitors multiple lumber surfaces and specifically targets three common sawing defects (taper, snipe/flare, and snake). Nontraditional control charts are suggested based on the decomposition of LRS measurements into trend, waviness, and roughness. The proposed charts can be used to monitor the slope parameter of a multiple linear regression model and the peak-to-peak waviness of observations from each board. Applying these methods should lead to process improvements in sawmills by better detecting common sawing problems and identifying the causes.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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