A Conceptual Model For the Preservation of Thick, Transgressive Shoreline Successions: Examples from the Forearc Nanaimo Basin, British Columbia, Canada
Bibliographic record
Abstract
Abstract Sequence stratigraphic models derived from passive margins and foreland basins characterize transgressive shorelines (excluding incised valleys) as recording periods of sediment starvation, and are commonly represented as thin (< 1 m) coarse-grained lags overlain by offshore mudstones. Preservation of thick, transgressive successions is relatively uncommon, but is possible in settings characterized by high sedimentation rates coupled with rapid base-level rise. Examples of such settings are found in deposits of the Upper Cretaceous Nanaimo Group, exposed on Vancouver Island and the Gulf Islands of British Columbia, Canada. The Nanaimo Basin is interpreted as a rapidly subsiding forearc basin that formed as a result of accretion of Wrangellia Terrane during subduction of the Farallon Plate beneath North America. The transgressive units of the Comox and Protection formations in the Nanaimo Basin display thick shoreline successions ranging from 3 m to 66 m, wherein base-level changes were dominantly controlled by tectonics. By comparing our results to previously studied transgressive successions and to mechanisms leading to transgression, a conceptual model for predicting transgressive architectures is proposed. Thick transgressive shoreline successions (> 10 m) are more likely to be developed in active tectonic settings (e.g., forearc and rift basins), which generally experience high sedimentation rates and relatively rapid changes in base level. Settings with high sedimentation rates and moderate base-level rise lead to progradational trends despite overall transgression of the shoreline. In settings where changes in eustatic sea level mainly control the resulting stratigraphic architecture (e.g., passive margins), thin transgressive successions (1–10 m) are more likely to be produced. Finally, in settings defined by relatively slow base-level change (e.g., foreland basins), the resultant transgressive succession typically comprises a lag (< 1 m) overlain by offshore mudstones. This conceptual model highlights the importance of the interplay between sedimentation rates and base-level rise, and is intended as a guide for the predictive modeling of sedimentary basins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".