Impacts of automation technology on quality of project deliverables in the Taiwanese construction industry
Bibliographic record
Abstract
The purpose of this study was to investigate the impacts of automation technology on project deliverables from the perspectives of various stakeholders. To address the primary aim, a survey was conducted to determine correlations between quality of project deliverables and automation adoption at the phase and task levels. This study also explored the links between automation utilization and project deliverables in detail. A second survey was used to identify common characteristics associated with the project deliverable-leveraging tasks. The analyses suggest that the quality of project deliverables is significantly associated with automation usage in the front-end, design, procurement, and construction phases. In addition, degrees of automation used in executing the project deliverable-leveraging tasks may have a significant impact on the correctness and completeness of project deliverables. The results also indicate that information and data intensive, management-related, and work-procedure-related characteristics can positively influence the quality of project deliverables.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".