Sensitivity of stoichiometric ratios in the Mississippi River to hydrologic variability
Bibliographic record
Abstract
Abstract The ratio of key elements such as nitrogen, phosphorus, and silica determines nutrient limitations that are important to regulating primary productivity and species composition in aquatic ecosystems. The flux of these nutrients in streams, as dissolved constituents or as particulate matter, is sensitive to variability in flow conditions. Most previous research on nutrient flux and hydrologic variability has focused on the response of individual elements, especially nitrogen, to changes in flow over time. This study examines how the ratios of total nitrogen to total phosphorus (N:P) and total nitrogen to dissolved silica (N:Si) respond to hydrologic variability in the Mississippi‐Atchafalaya River Basin. A doubling of the discharge by the Mississippi and Atchafalaya Rivers to the Gulf of Mexico is found to increase the N:P by 10% and the N:Si by 4%. Analysis of data from upstream stations indicates that the N:P increases with discharge in subbasins with intensive row crop agriculture and high fertilizer application rates but is less predictable in other subbasins. Conversely, the response of N:Si to discharge does not vary predictably with the land use characteristics of the subbasin. The response of the nutrient ratios to variability in flow may be linked to the different sources and sinks of each nutrient, as well as the difference between the dominant transport pathways of each nutrient. High‐resolution data and models that describe the dissolved and particulate nutrient cycling are needed to assess the relative contribution of different drivers to these observed patterns and to identify the response of nutrient ratios to hydrologic variability under future land use and climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".