Hot Spots and Hot Moments in Riparian Zones: Potential for Improved Water Quality Management<sup>1</sup>
Bibliographic record
Abstract
Vidon, Philippe, Craig Allan, Douglas Burns, Tim P. Duval, Noel Gurwick, Shreeram Inamdar, Richard Lowrance, Judy Okay, Durelle Scott, and Steve Sebestyen, 2010. Hot Spots and Hot Moments in Riparian Zones: Potential for Improved Water Quality Management. Journal of the American Water Resources Association (JAWRA) 46(2):278‐298. DOI: 10.1111/j.1752‐1688.2010.00420.x Abstract: Biogeochemical and hydrological processes in riparian zones regulate contaminant movement to receiving waters and often mitigate the impact of upland sources of contaminants on water quality. These heterogeneous processes have recently been conceptualized as “hot spots and moments” of retention, degradation, or production. Nevertheless, studies investigating the importance of hot phenomena (spots and moments) in riparian zones have thus far largely focused on nitrogen (N) despite compelling evidence that a variety of elements, chemicals, and particulate contaminant cycles are subject to the influence of both biogeochemical and transport hot spots and moments. In addition to N, this review summarizes current knowledge for phosphorus, organic matter, pesticides, and mercury across riparian zones, identifies variables controlling the occurrence and magnitude of hot phenomena in riparian zones for these contaminants, and discusses the implications for riparian zone management of recognizing the importance of hot phenomena in annual solute budgets at the watershed scale. Examples are presented to show that biogeochemical process‐driven hot spots and moments occur along the stream/riparian zone/upland interface for a wide variety of constituents. A basic understanding of the possible co‐occurrence of hot spots and moments for a variety of contaminants in riparian systems will increase our understanding of the influence of riparian zones on water quality and guide management strategies to enhance nutrient or pollutant removal at the landscape scale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".