Bedload Transport of Mud Across A Wide, Storm-Influenced Ramp: Cenomanian–Turonian Kaskapau Formation, Western Canada Foreland Basin—Discussion
Bibliographic record
Abstract
We would like to comment on a recent paper by Plint et al. (2012) that discusses mud transport across a Cretaceous shelf. The authors have taken to heart recent research that advocates looking for evidence of bedload transport of aggregated clays, and we are comfortable that much of the clay component in the Kaskapau Formation did indeed arrive via bedload transport rather than simply settling out of the water column. However, whereas we are perfectly comfortable with the proposed mode of transport, we do have several concerns and suggestions concerning one type of mud aggregate discussed by Plint et al. (2012). In the depositional model that the authors present (their fig. 16), they envision that cohesive mud is reworked by storms into intraclasts and that these then are carried across the seabed in bedload. That in itself is no problem, because it can be shown in experiments that surficial muds with as much as 85% water content can be transported as millimeter-size aggregates for considerable distances (Schieber et al. 2010). One class of aggregates, however, described as intraclastic aggregates (IAs for the remainder of this manuscript) by the authors, did capture our attention. The authors state that “ Storm wave reworking of the seafloor produced intraclastic aggregates…because the mud had been rendered cohesive by the chemical compaction and biostabilization processes that operated shortly after deposition .” We are aware that surficial sediments can gain enhanced cohesion and improved erosion resistance due to mucus from benthic worms and endo-sedimentary microbes, but we are not quite sure what the authors mean by chemical compaction. If they mean that cementation renders the muds firmer and more cohesive, it would have been rather appropriate to document this critical factor in the aggregates in question. By definition, …
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".