Temperature-drop sensor for determination of drying curves in conventional lumber drying
Bibliographic record
Abstract
Abstract Conventional lumber drying is carried out by forcing hot air to flow across a pile of lumber layers separated by wood strips. The airflow provides the heat required to warm up the lumber and produce the moisture evaporation and, in theory, the difference in temperature at each side of the load can be used to estimate the evaporation rate. The main problem with this approach is that typical temperature sensors that are installed in conventional kilns are not accurate enough to measure the temperature drop across the load during periods of low evaporation. In this paper, a new sensor to measure the temperature drop across the load is proposed and tested in three experimental drying runs of 2″×6″ spruce-pine lumber. The results demonstrate that after calibration, the temperature drop across the load can be used to determine drying curves in conventional lumber drying. In the particular case of this study, calibration was performed by multiplying the experimental temperature drop across the load by a constant factor, which was adjusted by identifying the correction factor that best simulated the experimental green moisture content of the three lumber charges.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".