Bibliographic record
Abstract
Abstract. Phosphorus (P) pollution in Lake Erie has been a growing concern, yet a comprehensive understanding of long-term P loss patterns is still lacking. We analyzed annual, monthly, and extreme daily P loss trends from 1974 to 2021 across eight major P-contributing tributaries using Seasonal Trend Decomposition with Loess (STL) and Generalized Extreme Value (GEV) models, and projected P loads through 2040 using machine learning (Bagging). Our results indicate no clear reduction in P loading from these tributaries over the past 47 years. Since the late 1980s, soluble reactive P (SRP) loads in the Sandusky and Maumee Rivers have increased from 0.12 and 0.67 tons day-1 to 0.41 and 1.55 tons day-1, respectively, with an increasing trend observed between January and June. We found that molar total nitrogen (N) to total P (TP) ratios in most tributaries, except for the Portage River, were generally 2–3 times higher than in the 1970s. Despite increased annual P loads, our analysis indicates a decline trend in daily P loads in the Maumee River during extreme flow events, except at the 2-year flow level, where daily TP tended to increase from 1.4 to 1.7 tons day-1 and SRP tended to increase from 0.2 to 0.3 tons day-1. Our future projections suggest that tributary P loads will continue to exceed target thresholds. In conclusion, this study addresses knowledge gaps in understanding long-term P dynamics in Lake Erie and highlights the need for more site-specific research to safeguard its water quality.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.027 | 0.025 |
| Insufficient payload (model declined to judge) | 0.128 | 0.108 |
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".