Tidal modulation of river-flood deposits: How low can you go?
Bibliographic record
Abstract
Abstract Quantification of the interaction between river discharge and tides is vital to characterize fluvio-deltaic systems, to identify diagnostic elements of tidal signatures in the rock record, and to reconstruct paleogeographies. In modern systems, even microtides can significantly influence delta morphodynamics; yet, many fundamental processes, particularly in prodeltaic settings, remain elusive. Here, by combining a unique process-product data set acquired during a flood event of the Po River (Italy) with numerical modeling, we show that tidal signatures are recorded in the open-water prodelta zone of a microtidal system. Based on the analyses of box-cores collected before and after a flood off the main distributary channel, we interpreted storm beds, tide-modulated flood strata of alternating normal and inverse graded beds, and rapid bioturbation. Modeling of the river discharge indicates that, at the peak of the flood, the steepening of the water-surface profile forced by 0.15 m lowering of sea level during low tides generated an 8% increase in river flow velocity. The alternation of profile steepness and associated cyclicity in flow strength during consecutive tidal cycles controlled the sediment load of the plume and, consequently, led to the deposition of tidal-modulated strata. Formation of microtidal signals appears to be enhanced in fluvio-deltaic successions characterized by multiple distributaries and in basins where river floods are out of phase with storm-wave activity. Bioturbation of sediment, which can start during the waning stage of the flow, and erosion by storm waves hamper the preservation of tidal signals, unless rapid burial occurs. The recognition of tidal-modulated strata in river-dominated settings may facilitate the characterization of mudstone reservoirs and reconstruction of paleogeographic conditions during deposition.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".