Changes in phosphorus concentration and flux from 2011 to 2023 in major U.S. tributaries to the Laurentian Great Lakes
Bibliographic record
Abstract
Reducing phosphorus (P) flux to the Great Lakes is critical for improving water quality and controlling eutrophication. We used 13 water years (2011–2023) of U.S. Geological Survey data from 24 major U.S. tributaries (representing 47% of the U.S. Great Lakes watershed area) to evaluate temporal changes in orthophosphate (PO 4 -P) and total P (TP) using Weighted Regressions on Time, Discharge, and Season. We assessed actual and flow-normalized P concentrations and fluxes. Between 2011 and 2023, P concentrations and fluxes declined in many tributaries, although the extent and significance of these declines varied. Decreases were more common and statistically likely for TP than PO 4 -P, and several high-loading watersheds had modest or non-significant changes. Flow-normalized PO 4 -P:TP flux ratios increased in over half the tributaries, suggesting that even where P reductions occurred, reductions in the more bioavailable P fraction were proportionally smaller. Actual P fluxes were strongly correlated with streamflow, and year-to-year variability in actual fluxes was, on average, three times greater than variability related to trends in flow-normalized fluxes. This underscores the role of hydrology in modulating P export and highlights how changing precipitation and runoff patterns can obscure or counteract management progress. Spring accounted for the largest share of annual P flux in most tributaries, though many showed declining spring contributions. Our basin-wide analysis reveals that while management efforts may have yielded progress in reducing TP in many watersheds, additional strategies would be needed to address PO 4 -P reductions and account for changing hydrology, especially in high-contributing watersheds.
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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 | 0.002 |
| 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".