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 paper presents an automated methodology for tracking earthmoving operations in near real time utilizing RFID technology to capture data during construction. It is based on attaching low cost passive RFID tags to hauling units (trucks) and attaching fixed RFID readers to designated gates of projects' dump areas. The RFID readers will identify and record the time each truck enters or exits one of these gates. The captured data will then be transferred wirelessly from the RFID reader to a computer housed in one of the temporary offices onsite and to the main server in contractor's head office. The collected data will be analyzed and processed automatically, without human intervention, to calculate the productivity of the hauling unit and report it directly to onsite personnel. Database application is developed to implement and automate the developed methodology in Microsoft Access. The developed database is used to process the data captured by the RFID-based system to calculate earthmoving productivity in near-real-time. It can also be used in estimating productivity of similar works during planning stage. The developed methodology is expected to facilitate early detection of discrepancies between actual and planned performances and supports project managers in taking timely corrective measures.
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 | 0.001 | 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