Repeatability limit comparison method of diagnostic ratios in oil fingerprint identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The diagnostic ratios play an important role in the oil fingerprint identification,and studies on the comparison method of diagnostic ratios have been developed in Europe,Canada,and China.This paper,based on the statistic conception of repeatability limit,the repeatability limit comparison method of diagnostic ratios in oil fingerprint were introduced,including the repeatability limit comparison principle,diagnostic ratios evaluation,and identification method.In addition,two identification examples were used in this paper to validate the method.The first identification was performed between a crude oil sample from sea oil platform and its artificial weathered oil sample.The second identification was carried out between two different crude oil samples,: one came from sea oil platform and the other came from land oilfield.The identification conclusion showed that the repeatability Limit comparison method of diagnostic was effectual and feasible in oil fingerprint identification,and significantly improved the precision and efficiency.
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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 | 0.006 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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