Anomaly detection using 1D convolutional neural networks for surface enhanced raman scattering
Bibliographic record
Abstract
An accurate supervised classification technique requires a large training database with an equal number of samples in each category. However, in practice, data class imbalance is naturally inherent in detection and identification tasks. In an extreme case, one category of data has a majority of training samples (positive class), causing over-classifying. In these circumstances, the negative classes are either absent, poorly sampled or not well defined. Deep one-class classifiers are artificial neural networks developed to overfit the positive class samples. This unique situation constrains the network model to be trained data features just with the knowledge of the positive class. One well-known application of one-class classifiers is for anomaly detection problem, where the model stands out outliers. In this study, we proposed using a one-dimensional CNN model for anomaly detection of Surface-Enhanced Raman Spectra applicable for Portable Raman Spectrometer in field investigations.
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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 | 0.000 |
| 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 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".