Testing the Application of Google Fusion Tables as a Collaborative Productivity Database and Benchmarking System for the Construction Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it