Bibliographic record
Abstract
The positive amplitude anomalies and base line drifts of self-potential (SP) curves make it difficult to recognize the amplitude anomalies and impossible to carry on the standardized calibration. In this way, it becomes extremely hard to read the amplitude anomalies of self-potential curves, increasing the difficulty in lithology recognition and stratigraphic correlation. What’s more, it is impossible to apply the computer technology to do the batch processing. Aimed at the practical problems, taking advantages of the collected digital logging data, positive self-potential anomaly recognition and transformation models have been established, converting the positive anomalies in the sandstone intervals into negative ones, solving the problem of two types of amplitude anomalies in the same scale range. Self-potential base line drift processing moves the biased base line to the null line step by step, adopting the method of piecewise fitting differential migration. With satisfying results, the conformation, amplitude and jugged degree are accordant with the primary curve, forming the technique of self-potential curve processing, laying foundation for logging translation and geological research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".