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
Nowadays, there is a huge increase in digital financial fraud as a result of the widespread usage of credit cards for online purchases. Therefore, a credit card fraud detection method with high accuracy, minimizing the risk of losing money when the transaction occurs, becomes imperative. The study first processed a synthetic data set, discarding useless features, using one hot encoding to convey categorical information to numerical ones and separating the training and testing datasets. Based on the new formed datasets, the study built three Machine Learning (ML) models for fraud detection, which are the Support Vector Machine (SVM) model, the logistic regression model and the decision tree model, respectively. Next, the study implemented on training these three models by fitting the model on training datasets and predicting the test data in testing datasets. Lastly, the heatmap named confusion matrix was provided to visualize the outcomes, indicating the accuracy of each model. Also, the study uses another four performance measurement scores which are Area under the Receiver Operating Characteristic Curve (ROC AUC score), accuracy classification score, balanced F1 score and precision score for evaluating models. Comparing these three models based on the confusion matrix and the four accuracy scores, the study explored that the decision tree model can achieve the best performance compared to other models in predicting fraud.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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