Data acquisition from construction sites for tracking purposes
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
Purpose The objective of this paper is to develop a tracking and control system that automates the process of data collection from construction sites for fast and accurate measurement of work progress. Design/methodology/approach The proposed system integrates different data acquisition hardware and software technologies including barcoding, radio frequency identification (RFID), laser distance and ranging (LADAR), digital images, and a tablet PC as integrating media. Findings The paper briefly highlights the advantages and limitations associated with each technology, and presents a methodology that best utilizes these technologies in an integrated system. At the core of the developed system is its database, which is designed to organize and store data collected from construction sites in a way that supports the developed methodology in progress reporting. Practical implications The accuracy and timeliness of these reports are crucial for management teams to take corrective actions, if needed, so as to assist in delivering projects on time and within budget. Originality/value The paper presents the layout of an IT platform designed to facilitate automated data acquisition from construction sites to support efficient time and cost tracking and control of construction projects. The system presented is capable of capturing text, numerical and graphical data to report efficiently on the progress of a project.
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 | 0.001 |
| 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