Improving Construction Supply Network Visibility by Using Automated Materials Locating and Tracking Technology
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
The accumulation of material buffers is commonly perceived within the construction industry as an effective means of shielding a project from the risks associated with uncertainty in the supply network. Much of the uncertainty arises out of a lack of visibility throughout the construction supply network, in which visibility refers to the level of awareness of the overall state of the supply network. The integration of Automated Materials Locating and Tracking Technologies (AMLTT) within the construction supply network presents a viable solution to this problem. This article presents the results of an investigation that examined the potential for AMLTT to increase work opportunities at the site level as a result of increased supply-network visibility and in turn reduce the dependency on material buffers. The investigation was completed by using a modeling and simulation approach grounded on a solid foundation of field data and experience. The results presented here are increasingly important as leaders in other industry sectors are beginning to report tangible benefits as a result of increased supply-network visibility as a result of the integration of AMLTT within their organizations’ supply networks.
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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