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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".