Measuring and Estimating Steel Drafting Productivity
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes an engineering productivity measurement system and proposes a neural network modeling approach for estimating the engineering productivity. The methodology has been applied to steel drafting. Firstly, the research focused on measuring the work scope of steel drafting projects. A method of quantitatively measuring the work scope was developed based on installed quantities, which is the quantity of steel pieces in terms of their physical characteristics. With the developed consistent measurement standard, a neural network model for estimating drafting productivity was developed and implemented using influencing factors appropriate to project conditions. Historical data collected through an implemented data acquisition system in a steel fabrication company were prepared for neural network training and model validation. This predictive model streamlines and increases the accuracy of earlier estimating process, which was highly subjective.
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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 | 0.000 | 0.000 |
| 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