A Chatbot System for Construction Daily Report Information Management
Bibliographic record
Abstract
A Chatbot System for Construction Daily Report Information Management Jehyun Choa and Ghang Lee Pages 429-437 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Consistently updating, analyzing, and managing construction-related information is one of the key success factors in project management. Quite a few construction projects have recently started to utilize instant messaging (IM) applications such as Slack, WhatsApp, and WeChat as a communication channel among project participants to share daily construction information due to easy accessibility. However, general contractors are still required to manually extract and integrate the data from instant messages to compose daily reports. This is because the data inputted by subcontractors through IM applications are usually in an unstructured form and the IM application is not normally interoperable with the systems database especially developed for construction management. To solve this problem, this study proposes a chatbot-assisted construction daily report data management system. The chatbot in the proposed system collects and processes the required information through conversations with subcontractors, and automatically generates and shares a daily report for general contractors. A prototype system has been designed and implemented to prove the concept. Keywords: Construction daily report; Chatbot; Instant messaging application DOI: https://doi.org/10.22260/ISARC2019/0058 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".