Up-Estuary Variation of Sedimentary Facies and Ichnocoenoses in an Open-Mouthed, Macrotidal, Mixed-Energy Estuary, Gomso Bay, Korea
Bibliographic record
Abstract
Abstract Integrated sedimentologic and ichnologic studies from the open-mouthed, Gomso Bay estuary on the western Korean coast have revealed that both tides and waves play an important role in estuarine sedimentation. Because of up-estuary decrease in wave energy, physical structures pass up-estuary from wave-dominated planar lamination and hummocky cross-stratification to tide-dominated heterolithic stratification. The infaunal distribution is sensitive to physiological stresses, and traces increase in size from the inner bay to the outer bay. The mappable trends in sedimentary facies and ichnofacies appear to be oblique to the estuarine margin in the outer and middle bays because of wave refraction, whereas facies belts in the inner bay are parallel to the estuary margin, reflecting tide-dominated conditions. Although useful estuarine facies models have been constructed from a growing number of modern and ancient studies, the estuarine classification schemes based on tidal range and geomorphic elements are apparently in conflict. Modern examples from Willapa Bay and this study confirm that the facies-belt model should be considered to be the most useful in applying the estuarine classification, and estuary morphology is related directly to the tidal prism rather than tidal range. In this context, the study results can be used to make interpretations of the geometry of coastlines and clastic reservoirs in ancient examples.
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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) | 0.001 | 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".