Estimation of δ and <i>C</i><sub>13</sub> of organic-rich shale from laser ultrasonic technique measurement
Bibliographic record
Abstract
The laser ultrasonic technique (LUT) has many advantages in dealing with the anisotropy of organic-rich shale over the transducer ultrasonic contacting measurement. We have developed a systematic procedure to estimate anisotropic parameter [Formula: see text] and elastic parameter [Formula: see text] in organic-rich shale from LUT measurement. A novel filtering method called the structural-oriented space-varying median filter (SOSVMF) is proposed for removing the erratic noise from the recorded data. We apply the proposed algorithm onto one synthetic example and real recorded data in artificial bakelite and organic-rich shale by using LUT to show the successful performance. Compared with the original recorded data, the denoised waveform by SOSVMF will also help to obtain more accurate parameter estimations of the anisotropic parameter [Formula: see text] and elastic parameter [Formula: see text]. More importantly, the presented systematic procedure may extend the application of LUT to the study of attenuation anisotropy in organic-rich shale and scale physical modeling. In addition, the surface-modification method may be an effective method to enhance the laser-ultrasonic conversion efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".