AI-Based Currency Exchange and Identification Model
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
American tourism is a multi-million dollar market. In 2022 alone, 80.7 million Americans traveled outside the United States of America. Since most of the destinations where American tourists travel, such as Mexico, Europe, and Canada, do not use U.S. currency, American tourists are subject to unnecessary stress due to this economic difference. There is a need for an identification system to classify different types of currency to aid in the American tourist’s travel experience. The proposed AI method collaborates with an online currency exchange rate database to update the rate of exchange for each currency to the US dollar in real-time. We applied our proposed method on currencies from Canada and Mexico; however, it could be extended to other currencies as well. The proposed model is trained on captured images of currencies, and the accurateness for the Mexican and Canadian currency models are 79% and 81%, respectively.
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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 | 0.000 |
| 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