Hybrid human resources localization and tracking system
Bibliographic record
Abstract
Organizational success and productivity depend on the effective and efficient utilization of Human Resources (HR). In view of the importance of HR, organizations put high premium on their safety, wellbeing and adequate monitoring. Several researchers have come up with solutions for monitoring HR movements but the major challenge has been the inability of a single technique to adequately and comprehensively monitor and provide accurate positioning data due to the changing environments of the workplace. This paper presents the implementation of a hybrid HR monitoring system using Global Positioning System (GPS), Radio Frequency Identification (RFID), cameras and sensors. The model is implemented using Python programming language as frontend and MySQL database management system as backend. Case study of the monitoring of selected staff of Information and Communication Technology Application Centre (ICTAC) of Adekunle Ajasin University, Akungba-Akoko, Nigeria was used to test the adequacy and practical functions of the model. Obtained data on positioning accuracy, signal sensitivity, cost of deployment and coverage area formed the basis for evaluation and comparative analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 0.003 |
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".