Mobile multimodal extensions to collaborative Web-based systems
Bibliographic record
Abstract
The development of leading-edge multimodal (e.g., concurrent text, voice, and video capabilities) mobile applications as a technology that holds promise for the construction industry is described. Because of the complexity of the products, the relatively short execution time frame, and the number of parties involved, the information exchanged throughout an architectural, engineering, and construction (AEC) project is extensive. Efficiently managing this information exchange is a significant impediment to increasing the overall productivity of the industry. The technology focus of this paper extends today's tools by addressing one of the unique characteristics of the industry: the need for mobility of real-time information in an integrated and collaborative environment. Mobile computing devices have the capabilities and characteristics for wide use in real-time communication of project information to project repositories or between project participants. As the industry moves away from the desktop and laptop Web paradigms toward the mobile Web paradigm, the availability of real-time complete information exchange with the project information repository presents new opportunities for decision-making in the AEC industry. To facilitate more widespread use of the solution applications described, extensive usability research in light of the various processes in the AEC industry is needed; a preliminary framework for this purpose is presented.Key words: project information management, collaborative information systems, mobile computing, multimodal functionality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".