Flow‐process controls on grain type distribution in an experimental turbidity current deposit: Implications for detrital signal preservation and microplastic distribution in submarine fans
Bibliographic record
Abstract
Abstract Deep‐water depositional systems are the ultimate sink for vast quantities of terrigenous sediment, organic carbon and anthropogenic pollutants, forming valuable archives of environmental change. Our understanding of the distribution of these particles and the preservation of environmental signals, in deep‐water systems is limited due to the inaccessibility of modern systems, and the incomplete nature of ancient systems. Here, the deposit of a physically modelled turbidity current was sampled ( n = 49) to determine how grain size and grain type vary spatially. The turbidity current had a sediment concentration of 17%. The sediment consisted of, by weight, 65% quartz sand (2.65 g/cm 3 ), 17.5% silt (2.65 g/cm 3 ), 7.5% clay (2.60 g/cm 3 ) and 5% each of sand‐grade garnet (3.90 g/cm 3 ) and microplastic fragments (1.50 g/cm 3 ). The grain size and composition of each sample was determined using laser diffraction and density separation, respectively. The results show that: (a) bulk grain size coarsened axially downstream on the basin floor challenging the notion that basin floor deposits fine radially from an apex upon becoming unconfined; (b) no sample composition matched the input composition of the flow, indicating that allogenic signals can be autogenically shredded and spatially variable in sediment gravity flow deposits; and (c) microplastic fragments were concentrated in levee and lateral basin floor fringe positions; however, microplastic concentrations in these positions were lower than input, suggesting microplastics bypassed the sampled positions. These findings have implications for: (a) the development of ‘finger‐like’ geometries and facies distributions observed in modern and ancient systems; (b) interpreting environmental signals in the stratigraphic record; and (c) predicting the distribution of microplastics on the sea floor.
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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".