The MOBB experiment: A prototype permanent off‐shore ocean bottom broadband station
Bibliographic record
Abstract
Technical accomplishments of the past 10 years in the design and deployment of sea floor broadband seismic systems are now making it possible to start addressing the issue of the limited coverage of the Earth that can be achieved through land‐based installations, at the regional or global scale. In particular, the September 2002 Ocean Mantle Dynamics (OMD) workshop in Snowbird, Utah [OMD Workshop Committee, 2003] proposed the development of two “leap‐frogging arrays” of about 30 broadband sea floor instruments to fill geophysically important target holes in ocean coverage for deployment periods of 1 to 2 years. The rationale for an off‐shore (“Webfoot”) component of the SArray/Earth‐scope “Bigfoot” array was also highlighted at this meeting, pointing out that the study of the North American continent should not stop at the ocean margin. The ocean floor environment is challenging for broadband seismology for several reasons. Broadband seismometers cannot be simply “dropped off” a ship with the expectation that they will produce useable data, particularly on the horizontal components. Several pilot experiments, [e.g., Montagner et al., 1994; OSN1, 1998; Suyehiro et al., 2002] have addressed the issue of optimal installation of ocean bottom stations, and in particular, have carried out comparisons between borehole, sea floor, and buried sea floor installations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".