An Integrated RFID and Barcode Tagged Item Inventory System for Deployment at New Brunswick Laboratory
Bibliographic record
Abstract
New Brunswick Laboratory (NBL) has a numerous inventory containing thousands of plutonium and uranium certified reference materials. The current manual inventory process is well established but is a lengthy process which requires significant oversight and double checking to ensure correctness. Oak Ridge National Laboratory has worked with NBL to develop and deploy a new inventory system which utilizes handheld computers with barcode scanners and radio frequency identification (RFID) readers termed the Tagged Item Inventory System (TIIS). Certified reference materials are identified by labels which incorporate RFID tags and barcodes. The label printing process and RFID tag association process are integrated into the main desktop software application. Software on the handheld computers syncs with software on designated desktop machines and the NBL inventory database to provide a seamless inventory process. This process includes: 1) identifying items to be inventoried, 2) downloading the current inventory information to the handheld computer, 3) using the handheld to read item and location labels, and 4) syncing the handheld computer with a designated desktop machine to analyze the results, print reports, etc. The security of this inventory software has been a major concern. Designated roles linked to authenticated logins are used to control access to themore » desktop software while password protection and badge verification are used to control access to the handheld computers. The overall system design and deployment at NBL will be presented. The performance of the system will also be discussed with respect to a small piece of the overall inventory. Future work includes performing a full inventory at NBL with the Tagged Item Inventory System and comparing performance, cost, and radiation exposures to the current manual inventory process.« less
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".