Estimation of helical fiber pitch angle and trace spacing from colocated DAS, accelerometer, and geophone datasets
Bibliographic record
Abstract
Distributed acoustic sensing (DAS) data recorded on a fiber loop containing straight and helically wound fiber cables with different indices of refraction poses challenges for determining the trace spacing to use for geometry assignment and dataset registration. Assuming the actual helical pitch angle may be different than the nominal pitch angle, we propose a method to estimate pitch angle and helical trace spacing by cross-correlation with co-located datasets. For a cable with a nominal pitch angle of 30.0 degrees (520 traces per 300 m), cross-correlation with straight fiber data gives estimated pitch angles of 29.6 degrees for helical fiber in a vertical well (507 traces per 300 m) and 28.5 degrees for the same helical fiber in a horizontal trench (502 traces per 300 m). Substituting accelerometer and geophone datasets for straight fiber data does not yield valid estimates of pitch angle, but the estimated trace spacings for helical fiber data are within 3 cm (or less) of those obtained from the straight fiber data.
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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 | 0.000 | 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) | 0.002 | 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".