Causes of rapid mixing at a junction of two large rivers: Río Paraná and Río Paraguay, Argentina
Bibliographic record
Abstract
Airborne and satellite observations show that when large rivers join they can take hundreds of kilometers to mix completely but, on occasion, may mix very rapidly. Application of established semitheoretical analyses shows that long mixing lengths should be expected. The first measurements of mixing processes at a large river junction (Río Paraná and Río Paraguay, Argentina, combined width ∼2.8 km) are presented at two occasions: first when they mix in >400 km, and second when mixing is complete in only 8 km downstream of the junction. For the case of slower mixing, at‐a‐point surveys showed that mixing driven by turbulent shear associated with a near‐vertical shear layer was restricted to close to the junction (to 0.272 multiples of the postconfluence width downstream). Transect surveys showed penetration of more turbid water from the Río Paraguay underneath the Río Paraná, but this was insufficient to promote more rapid mixing. There was no clear channel‐scale circulation present and slow mixing was compounded by reverse topographic forcing on the mainstream Río Paraná side of the river. This kept more turbid water on the Río Paraguay side of the river, close to the bed. In the case of rapid mixing, we found clear channel‐scale circulation. The momentum ratio between the combining flows reinforced the effects of the discordance in bed height between the tributaries at the confluence and allowed penetration of more turbid Río Paraguay water further across the channel width deeper within the flow. The importance of the interaction between momentum ratio and bed morphology at channel junctions makes mixing rates at the confluence dependent upon basin‐scale hydrological response, which is more likely to differ between large confluent rivers than small rivers, as a result of the different climatic/topographic zones that they may capture.
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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 | 0.001 |
| 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 | 0.001 | 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".