Real-Time Recognition and Feature Extraction of Stratum Images Based on Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
Accurate identification and feature extraction of stratum images play a crucial role in geological exploration, resource prospecting, and mining operations.Traditional methods of stratum image identification largely rely on human experience and manual operations, which are inefficient and prone to errors.In recent years, deep learning technology has provided new methods for the identification and feature extraction of stratum images, but existing deep learning models still face challenges in computational efficiency, multi-scale feature extraction, and uneven sample distribution.This paper proposes a stratum image feature extraction network based on the pyramid model and constructs a lightweight stratum identification model for real-time recognition.By introducing a classification-regression network structure and anchor-based sample supervision rules, this study aims to improve the accuracy and efficiency of the model, providing an effective solution for real-time recognition of stratum images.
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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 it