EFFECTIVE SEMANTIC WEB-BASED SOLUTIONS FOR CIVIL ENGINEERING
Bibliographic record
Abstract
The World Wide Web (the Web) has become widely recognized as the primary channel of communication and dissemination of information in industry. It has made immense amounts of information available and has been gradually accepted by users who now readily incorporate it into their daily lives. In civil engineering, despite the wide use of the Web, its use is still limited by the users’ willingness and ability to share their knowledge. The Semantic Web challenges this problem as it adds meaningful descriptions to information in a manner that facilitates automated analysis and extraction by computer systems. While many scholars in the field realize the importance of using such technologies to promote collaboration with various parties including the general public, research that is conducted on the Semantic Web and similar technologies is often disconnected from its application. Nevertheless, there is general consent that this collaboration is essential for the creation of sustainable solutions in civil engineering. This paper reviews and analyzes current research being conducted on this area in Europe and North America. We also propose methods that involve the Semantic Web to improve usability and effective information flow in city-scale projects.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".