Mixing as a driver of temporal variations in river hydrochemistry: 2. Major and trace element concentration dynamics in the Andes‐Amazon transition
Bibliographic record
Abstract
Abstract Variations in riverine solute chemistry with changing runoff are used to interrogate catchment hydrology and to investigate chemical reactions in Earth's critical zone. This approach requires some understanding of how spatial and temporal averaging of solute‐generating reactions affect the dissolved load of rivers and streams. In this study, we investigate the concentration‐runoff (C‐Q) dynamics of a suite of major (Na, Mg, Ca, Si, K, and SO4) and trace (Al, Ba, Cd, Co, Cr, Cu, Fe, Ge, Li, Mn, Mo, Nd, Ni, Rb, Sr, U, V, and Zn) elements in nested catchments of variable size, spanning the geomorphic gradient from the Andes Mountains to the Amazon Foreland‐floodplain. The major elements exhibit various degrees of dilution with increasing runoff at all sites, whereas the concentrations of most trace elements either increase or show no relationship with increasing runoff in the three larger catchments (160–28,000 km2 area). We show that the observed main stem C‐Q dynamics are influenced by variable mixing of tributaries with distinct C‐Q relationships. Trace element C‐Q relationships are more variable among tributaries relative to major elements, which could be the result of variations in geomorphology, lithology, and hydrology of the subcatchments. Certain trace metals are also lost from solution during in‐channel processes (possibly related to colloidal size‐partitioning), which may exert an additional control on C‐Q dynamics. Overall, we suggest that tributary aggregation effects should be assessed in heterogeneous catchments before C‐Q or ratio‐Q relationships can be interpreted as reflecting catchment‐wide solute generation processes and their relationship to hydrology.
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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 | 0.000 | 0.000 |
| 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".