Predicting unit energy consumption during industrial veneer drying via data-driven approaches
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
Veneer drying usually consumes a significant amount of energy including heat and electricity. The soaring energy price as well as the substantial social-environmental concerns regarding energy use have urged veneer manufacturers to adapt and become more efficient in energy consumption. Different from the physics-based methods commonly seen in the literature, this research embraced a data-driven approach to analyze and predict unit gas and electricity consumption during industrial veneer drying. Both linear regression and random forest (RF) algorithms were deployed for prediction. Based on cross-validation evaluations, the RF model with all explanatory variables slightly outperformed two linear models regarding almost all accuracy metrics, although linear models had the advantage of providing an easy-to-interpret solution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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