Using Model Trees to Represent Knowhow of Experienced Estimators in Steel Fabrication Industry
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
To facilitate knowledge representation and transfer, this research explores the feasibility of implementing the machine learning technique of model trees in a practical application setting. In collaboration with a steel fabricator company in Canada, we tapped into the mental model of experienced estimators in the steel fabrication domain by analyzing pre-bid estimate data and evaluating the performance of model trees alongside other mainstream machine learning methods. The resulting model was validated by comparing its logic against that of the experienced estimator, demonstrating close alignment. Additionally, the model delivered reliable prediction accuracy. Our case study concludes that the technique of model trees is capable of generalizing hidden patterns and implicit relationships in the training data; to a certain degree, the model trees has generated a sufficient and explicit representation of the know-how of experienced estimators in the industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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