Tidal dynamics in palaeo-seas in response to changes in physiography, tidal forcing, and bed shear stress
Bibliographic record
Abstract
Abstract Simulating hydrodynamic conditions in palaeo‐ocean basins is needed to better understand the effects of tidal forcing on the sedimentary record. When combined with sedimentary analyses, hydrodynamic modelling can help inform complex temporal and spatial variability in the sediment distribution of tide‐dominated palaeo‐ocean basins. Herein, palaeotidal modelling of the epicontinental Upper Jurassic (160 Ma, lower Oxfordian) Sundance and Curtis seas of North America reveals possible regional‐scale variations in tidal dynamics in response to changes in ocean tidal forcing, physiographic configuration and bottom drag coefficient. A numerical model forced with an M2 tidal constituent at the open boundary shows that the magnitude and location of tidal amplification, and the variability in current velocity and bed shear stress in the basin, were controlled by palaeophysiography. Numerical results obtained using a depth of 600 m at the ocean boundary of the system enable the prediction of major facies trends observed in the lower Curtis Formation. The simulation results also highlight that certain palaeophysiographic configurations can either permit or prevent tidal resonance, leading to an overall amplification or dampening of tides across the basin. Furthermore, some palaeophysiographic configurations generated additional tidal harmonics in specific parts of the basins. Consequently, similar sedimentary successions can emerge from a variety of relative sea‐level scenarios, and a variety of sedimentary successions may be deposited in different parts of the basin in any given relative sea‐level scenario. These results suggest that the interpretation of sedimentary successions deposited in strongly tide‐influenced basins should consider changes in tidal dynamics in response to changing sea level and basin physiography.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".