Going Mobile: Field Force Computing Improves Productivity
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
This article presents an AwwaRF Tailored Collaboration research report titled “Field Computing Applications and Wireless Technologies for Water Utilities”, that analyzes current use by water utilities of field computing applications and mobile technologies. The report takes a closer look at the technologies and work practices in place at several U.S. utilities and provides an overview of current and emerging field computing and wireless technologies on the market. The research methodology included a comprehensive literature review, a survey of U.S. and Canadian AwwaRF‐member water utilities, case studies from five water utilities, and a review of secondary research about current and emerging technologies for the utility sector. The report focused on three key components of mobile technology: benefits, challenges, and technology. Mobile resource management (MRM), an emerging category of business solutions that enhances efficiency, asset management, and customer service, is discussed along with real‐world application of field computing technology. Two examples of utilities using mobile technology are provided.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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