Analyzing Scaffolding Needs for Industrial Construction Sites Using Historical 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
Industrial construction includes a wide range of construction projects, such as petroleum refineries and chemical plants. These involve several trades, such as civil, mechanical, and electrical. Different trades carry out different tasks on these projects, and often depend on scaffolds to access their work areas. Quantification of scaffold requirements of large projects is difficult because of variability in work area heights and congestion and the multiple trades that need to be serviced by the scaffold system. Traditional estimating methods rely on percentages of direct trade hours or volume of work area and usually result in significant deviation from real scaffold costs. The study presented in this paper aims to develop better understanding and estimates of scaffold needs for industrial construction sites, based on analysis of data collected from a mega-project over the course of two and a half years by a major contractor. The study seeks to discover patterns and reliable correlations that may exist between required scaffold hours and other work attributes that can allow for development of a reliable estimation model. The paper presents the results of initial analysis and exploration of data mining experiments, in addition to the challenges faced and future research recommendations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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