Marine controlled source electromagnetic methods for gas hydrate assessment: New instrumentation and first results from the Black Sea test cruise
Bibliographic record
Abstract
The electrical conductivity of the seafloor derived from marine controlled source electromagnetic (CSEM) data can be related to the gas hydrate concentration and distribution within the stability zone, and can provide valuable information for resource assessment. Gas hydrates are electrically resistive in contrast to the conductive pore fluid and may significantly increase the bulk resistivity where they occur in sufficient quantities. The marine electromagnetic group at BGR has developed a new, seafloor-towed, electrical multi-receiver system that consists of a transmitting dipole and four receiving dipoles at increasing offsets, sensitive to depths relevant for gas hydrate evaluation from the seafloor to some hundred meters below. The system was successfully deployed on a test cruise on RV Poseidon in water depth of 1335 m over known gas hydrate targets in the Danube Delta, and in shallow water on the upper rim of the Danube Canyon. Inversion of the data reveals highly anomalous resistivity which at least for the deep water deployment can be explained with a combination of fresh pore water and the presence of gas hydrates in the sediments.
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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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 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".