Improvements in the Efficiency of Ocean Bottom Sensor Surveys through the Use of Multiple Independent Seismic Sources
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
Summary Improving the efficiency of ocean bottom sensor (OBS) surveys has been a long time goal of both oil companies and acquisition contractors. Full azimuth and offset OBS surveys have historically been time consuming to acquire due to limitations in the amount of equipment and the efficiency of the crews. A large increase in efficiency can be realized through the use of multiple independent source vessels working simultaneously. The field of Compressive Sensing (CS) provides the necessary framework to allow the vessels to work simultaneously and be able to produce comparable data sets to a traditionally acquired OBS survey. We describe the operational issues of acquiring a survey with multiple source vessels including the CS techniques used.
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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 | 0.002 | 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