Bibliographic record
Abstract
The dataset plays a vital role in model training. It is commonly believed that larger datasets improve accuracy. However, if we cannot ensure the quality of the data, it not only consumes resources but can also lead to over-fitting. To address this issue, this thesis proposes eight methods on two datasets, which range from image similarity algorithms to clustering CNN features, to create the smallest possible subsets of data. We evaluated different scenarios for each method and compared the results with those obtained using the corresponding full dataset and random removal, determining which data should be retained and which discarded. The empirically observed generalization gap resulting from dataset pruning is substantially consistent with our theoretical expectations. The proposed method can reduce data from both datasets by 20% with almost no loss in accuracy. In fact, a 2.3% increase in accuracy is observed for dataset A even with the 20% removal. The method effectively reduces the smaller dataset by 60% and the larger dataset by 40%, while maintaining a drop in accuracy of less than 2%. Additionally, if a decrease in test accuracy of 4.6% for the smaller dataset and 4.8% for the larger dataset is deemed acceptable, it is possible to reduce the data from both datasets by 70%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".