Total and dissolved phosphorus losses from agricultural headwater streams during extreme runoff events
Bibliographic record
Abstract
Eutrophication continues to be a concerning global water quality issue. Managing and mitigating harmful algal blooms demands clear information on the conditions promoting large phosphorus losses from contributing watersheds. Of particular concern is the amount and form of phosphorus loading to receiving water bodies during extreme runoff events, which are expected to increase in frequency due to climate change. Five years (2015 to 2020) of water quantity and quality data from 11 agricultural watersheds in the lower Great Lakes basin were analyzed and used to model total and dissolved phosphorus losses. This study aimed to assess temporal dynamics in phosphorus concentrations and losses over runoff events covering a wide range of hydrologic conditions and to quantify their relative importance on annual phosphorus losses. Event concentration-discharge relationships for total and dissolved phosphorus were hysteretic and had contrasting dominant patterns across watersheds. The proportion of annual phosphorus losses during events was highly variable between watersheds, accounting for 47-94 %. Extreme events were particularly impactful: as few as three events per year were found to be responsible for nearly half of total phosphorus (20-50 %) and total dissolved phosphorus (14-44 %) losses. Variability in total and dissolved phosphorus losses and concentrations over a wide range of flow conditions suggests that event magnitude is an important control on the relative mobility of particulate and dissolved phosphorus fractions. This study showed that insights into nutrient dynamics and phosphorus budgets in the lower Great Lakes basin and agriculture dominated environments more broadly can be gained by assessing event nutrient losses with respect to flow conditions and patterns in concentration-discharge relationships.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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".