Extreme flood event and their depositonal signatures: the case of the Storm Alex in the Roya Valley
Bibliographic record
Abstract
This study examines sedimentary deposits from Storm Alex (2 October 2020) in the Roya Valley, focusing on three different sub-valley (Dente, Consciente and Caïros) to understand flow processes and associated lithofacies. Key factors controlling sediment transport include lithology, slope, and sediment supply, which influence the occurrence of bedload, suspension, or debris flows. A notable ~5 wt% difference in fines (clay + silt) was observed between debris flows in the Dente sub-valley and bedload/hyperconcentrated flows in other areas.In the Dente, extensive reworking of glacial and colluvial deposits triggered debris flows that transitioned into hyperconcentrated and bedload flows, culminating in sheetflood deposits on the Viévola fan. The Consciente sub-valley exhibited bedload and hyperconcentrated flows, with debris flows linked to lateral inputs from landslides. The Caïros sub-valley, characterised by gentler slopes and a wider valley floor, was dominated by bedload processes with localized debris flows originating from right-bank tributaries or natural dams.Hydraulic reconstructions using empirical discharge, unit stream power, shear stress, and clast size estimates provided insights into event intensity, offering valuable reference points for understanding extreme hydro-sedimentary events. Spatial and temporal variability was significant, highlighting the challenges in interpreting fossil deposits without precise temporal context, since a single extreme rainfall event (>1000-year return period) produced a wide range of facies.This case study underscores the complexity of flow transitions (debris flows to bedload) and the importance of lithological and topographic constraints. The findings emphasize the value of interdisciplinary, multi-scale approaches in documenting and understanding extreme hydro-sedimentary events in mountainous regions
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".