Bibliographic record
Abstract
Gravity is measured presently on the sea surface and on the sea floor. Surface gravity suffers from loss of resolution over the deep ocean because the perturbing source masses are far from the observer. Bottom measurements recover this resolution, but suffer from poor coverage because of the time needed for each measurement. We have constructed a gravimetry system that combines the rapid data collection capability of a moving platform with the high resolution gained by locating the observations near the bottom. This gravity sensor is tethered to a ship and towed just above the sea floor. The instrument consists of a LaCoste and Romberg shipboard gravity meter modified to fit inside a pressure case that is mounted on a platform designed for towing stability. We have tested it in a survey in the San Diego Trough, a 1000-m-deep sedimented valley in the Pacific Ocean in the California continental borderlands. Multiple gravity tracklines collected there at a depth of 935 m show a resolution of a few tenths of a mGal. The new instrument will be useful for surveys of features whose lateral extent is equal to or less than the ocean depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.011 | 0.004 |
| Insufficient payload (model declined to judge) | 0.992 | 0.995 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".