Hybrid human resources localization and tracking system
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
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.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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