An implementation model for automated construction materials tracking and locating
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
Good materials management on large construction projects is critical for maximizing productivity and project performance. When key materials are temporarily lost, whole crews may be left idle and the project may be delayed. When key materials are completely lost, the impact can be enormous. In fact, one of the major problems in managing construction materials and equipment is tracking them in the supply chain and knowing their location on large job sites. Fortunately, location can now be automatically estimated within metres using emerging technologies. This paper proposes a general implementation model for automated construction materials tracking and locating on large industrial projects, such as refineries and power plants. It includes a methodology for determining what type of technology should be used for different types of projects and construction materials. It is based on an analysis of the capabilities of emerging technologies and on experience gained from implementing automated materials tracking prototypes on two large industrial projects. It is concluded that these technologies can produce substantial net benefits, if implemented properly on the right projects using the model described here.
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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