Phosphorus export dynamics and hydrobiogeochemical controls across gradients of scale, topography and human impact
Bibliographic record
Abstract
Abstract Concentration‐discharge (c‐Q) plots are routinely used as an integrated signal of watershed response to infer solute sources and travel pathways. However, the interpretation of c‐Q data can be difficult unless these data are fitted using statistical models. Such models are frequently applied for geogenic solutes, but it is unclear to what extent they might aid in the investigation of nutrient export patterns, particularly for total dissolved phosphorus (TDP) which is a critical driver of downstream eutrophication problems. The goal of the present study was therefore to statistically model c‐Q relations (where c is TDP concentrations) in a set of contrasting watersheds in the Northern Great Plains—ranging in size from 0.2 to 1000+ km 2 —to assess the controls of landscape properties on TDP transport dynamics. Six statistical models were fitted to c‐Q data, notably (a) one linear model, (b) one model assuming that c‐Q relations are driven by the mixing of end‐member waters from different landscape locations (i.e., hydrograph separation), (c) one model relying on a biogeochemical stationarity hypothesis (i.e., power law), (d) one model hypothesizing that c‐Q relations change as a function of the solute subsurface contact time (i.e., hyperbolic model), and (e) two models assuming that solute fluxes are mostly dependent on reaction rates (i.e., chemical models). Model performance ranged from mediocre ( R 2 < 0.2) to very good ( R 2 > 0.9), but the hydrograph separation model seemed most universal. No watershed was found to exhibit chemostatic behaviour, but many showed signs of dilution or enrichment behaviour. A tendency toward a multi‐model fit and better model performance was observed for watersheds with moderate slope and higher effective drainage area. The relatively poor model performance obtained outside these conditions illustrates the likely importance of controls on TDP concentrations in the region that are independent of flow dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".