Differentiating hyperpycnal, hypopycnal and turbidity current deposits in late Quaternary glaciogenic mud
Bibliographic record
Abstract
Abstract The fate of sediment‐laden density flows is strongly controlled by the density contrast between the flow and the basin fluid, which fundamentally influences the character of the sedimentary deposit. However, differentiating the deposits of hyperpycnal and hypopycnal flows, in addition to slope‐failure‐related turbidity currents, remains a source of debate. In part, this can be attributed to the difficulty in determining the density of the fluid in an ancient depositional basin. In this study of a nearly modern succession of fine‐grained (silt‐clay) glaciogenic deposits, basin fluid conditions are well‐constrained and the sedimentological make‐up of these sediments was determined using X‐ray computed tomography and microscopy. Deposits of bottom‐hugging hyperpycnal flows consist of a distinctive alternating pattern of silt‐rich and clay‐rich laminae attributed to the rhythmic alternation of shear thinning and shear thickening processes in the mm‐ to sub‐mm‐thick, non‐Newtonian very‐near‐bed region. A similar pattern is observed in fine‐grained turbidites, suggesting similarity in depositional mechanism. This similarity suggests that differentiating the deposits of fine‐grained hyperpycnal flows from turbidity currents based solely on physical stratal attributes is challenging. Alternatively, hypopycnal flows form buoyant plumes from which sediment settles, spawning bottom‐hugging secondary turbidity currents. In these flows, insufficient sediment concentration and shear stress prevent shear thinning and shear thickening processes from operating, and instead, entropic Brownian fluid motion results in an unstratified deposit with a disorganised fabric. Additionally, these strata contain irregularly spaced, well‐sorted silt lenses that range from a single silt grain to a few silt grains thick and record outer flow disturbances that reworked the previously deposited silt and clay. Whereas differentiating fine‐grained hypopycnal flow deposits from hyperpycnal flows or turbidity currents may be straightforward, differentiating them from pure suspension fallout would appear to rely on the recognition of the thin, well‐sorted silt lenses indicating advection rather than pure particle settling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".