Analysis of Terrestrial Hyperconcentrated Flows and their Deposit
Bibliographic record
Abstract
The term hyperconcentrated flow refers to intermediate states between debris flows and fluid flows, where fluid turbulence remains an important dispersal mechanism of clastic particles. Whereas the end-member flows are fairly well understood, unanimous agreement has not been reached on the subdivision and boundary definition of hyperconcentrated flows, both in meaningful rheological terms and, more so, in terms of the characteristics of their deposits. This paper briefly reviews the main characteristics of hyperconcentrated flows resulting from either suspended-load hyperconcentration or bedload hyperconcentration (traction carpet), and focuses on the analysis of three deposits possibly associated with these flows. The first two deposits formed in ancient, temperate alluvial fans of Pliocene–Pleistocene post-collision basins of the Northern Apennines, Italy, and the third in Upper Pleistocene glacial outwash of Ontario, Canada. The main finding is that geomorphological setting, climate and substrate geology are the prime control for hyperconcentrated flows in terms of frequency, magnitude and rheological properties of the flow. Recognition of hyperconcentrated-flow deposits through facies analysis leads to a better understanding of the developmental dynamics of alluvial sediment succession, and provides powerful information for risk assessment of localities possibly affected by hydrogeological hazards.
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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 | 0.003 | 0.003 |
| 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) | 0.001 | 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".