Bibliographic record
Abstract
Abstract Most submarine fans are supplied with both sand and mud, but these become segregated during transport, typically with the sand becoming concentrated in channels and channel-termination lobes. New data from high-resolution seismic reflection surveys and Deep Sea Drilling Project (DSDP)/Ocean Drilling Program (ODP) wells from a variety of fans allow a synthesis of the architecture of those submarine fans that have important sand deposits. By analyzing architectural elements, we can better understand issues important for petroleum geology, such as the reservoir properties of the sand bodies and their lateral continuity and vertical connectivity. Our analysis of fan architecture is based principally on the Amazon and Hueneme fans, generally perceived to be classic examples of muddy and sandy systems, respectively. We recognize depositional elements, for example, channel deposits, levees, and lobes, from seismic reflection data and document sediment character in different elements from DSDP/ODP drill cores. We show the utility for petroleum geology of evaluating sandy and muddy elements rather than characterizing entire fans as sand rich or mud rich. We suggest that fan classification should include evaluation of source-sediment volumes and grain size, as well as the probable processes of turbidity-current initiation, because these factors control the character of fan elements and their response to changes in sea level, sediment supply, and autocyclic changes in channel pattern. Basin morphology, controlled by tectonics, influences overall geometry, as well as the balance between aggradation and progradation.
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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 | 0.000 |
| 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) | 0.002 | 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".