Testing the Application of Google Fusion Tables as a Collaborative Productivity Database and Benchmarking System for the Construction Industry
Bibliographic record
Abstract
Productivity benchmarking has been an elusive element for construction companies around the world. With the advent of the Internet, many companies have attempted Web-based database and benchmarking efforts since the late 1990s. However, an industry consensus on a collaborative database and benchmarking tool has yet to be established. New opportunities are being generated for efficient and free database services that are challenging the established paradigm. Google fusion tables (GFT) Web application was launched in 2009 and has been evolving ever since release. GFT provides a user-friendly, collaborative, and interactive database tool that is available to the public on an international scale at no charge. This tool provides users with options to publish their data on another site, make it publically available and discoverable by search engines, or keep it private. The research presented in this paper aims to develop a pilot database using GFT for the storage and management of data from work-sampling observations for designated work trades on a project located in Calgary, Alberta, Canada. Data were uploaded to the Google cloud to commence the pilot database. The collected data were complemented further by the tool's charts and shared with participating parties. The application of GFT as a data management system was tested against a selected company's extranet and individually assessed through the survey of the users of the pilot program.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".