Effect of Mislabeled Data on Judgement Results for Re-bar Corrosion by Impact Sound Based on Neural Network
Bibliographic record
Abstract
The purpose of this study is to examine the effect of mislabeled data in the training data on the judgment results for reinforcement corrosion by the impact sounds of a steel ball colliding based on a neural network.For this purpose, the impact sounds of RC specimens with different corrosion levels were recorded, and the effects of contaminating with data in which corrosion has progressed beyond the target corrosion level into the positive training data were examined.As a result, it was found that the true positive rate decreased as the contamination rate increased when mislabeled data in the judgement the corrosion level of 1% was included.In addition, in the judgement of the corrosion level of 3%, the true positive rate tends to reduce when mislabeled data is included, but it was clarified that it is less affected by contaminating with the mislabeled data than the judgement of the corrosion level of 1%.
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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".