<i>In situ</i> measurements of compressional and shear wave properties during gravity coring operations
Bibliographic record
Abstract
Sediment cores provide valuable insight about the physical properties of the seabed, and laboratory measurements of sediment wave speed from cores are often considered “ground truth.” However, sound-speed estimates obtained from cores can be inaccurate due to changes in pressure, temperature, and mechanical properties of the sediment caused by the removal of the core from the seabed and its subsequent transport to the laboratory. To address these issues, the Acoustic Coring System (ACS) was developed. The system uses sets of transducers mounted below the penetrating tip of a sediment corer to make in situ measurements of geoacoustic properties as the corer penetrates the seabed. Compressional wave measurements are obtained with rod-mounted piezoelectric cylinders, and shear wave measurements are obtained with bender elements mounted in flat blades. The ACS was deployed during the environmental survey for ONR Seabed Characterization Experiment (SBCEX) in 2016 and in support of the ONR Canada Basin Acoustic Propagation Experiment (CANAPE) in 2017. In both cases, the system provided measurements of the sediment sound-speed profile as a function of depth within the seabed. On-going work focused on the measurement of shear wave speed profiles and compressional and shear wave attenuation will also be presented. [Work supported by ONR.]
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".