On Data Analysis and Design and Implementation of Data Preprocessing Scheme Based on Low-quality Rock Datasets
Bibliographic record
Abstract
With fast progress of deep learning technology, breakthroughs are achieved in many industries by virtue of efficient artificial intelligence models. In addition, computer hardware is cheaper, which makes it easier to acquire an excellent deep learning model. However, output of a model with good generalization rests with not only powerful hardware computing speed, but also quality of dataset involved in the calculation. Unfortunately, high-quality dataset is probably more expensive than high-end hardware, and this forces deep learning engineers or practitioners to use lower-quality dataset. Anyway, it doesn’t mean excellent deep learning programs can’t be created by such dataset. In particular, dataset preprocessing is equally important, and even engineers need to spend most of time elaborately formulating preprocessing strategies. This study mainly analyzes data and formulates preprocessing schemes of low-quality rock datasets. It aims to make deep learning programs more efficient and general-purpose at the lowest possible cost.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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 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".