Increased nutrient concentrations in Lake Erie tributaries influenced by greenhouse agriculture
Bibliographic record
Abstract
Greenhouse production of vegetables is a growing global trade. While greenhouses are typically captured under regulations aimed at farmland, they may also function as a point source of effluent. In this study, the cumulative impacts greenhouse effluents have on riverine macronutrient and trace metal concentrations were examined. Water samples were collected Bi-weekly for five years from 14 rivers in agriculturally dominated watersheds in southwestern Ontario. Nine of the watersheds contained greenhouses with their boundaries. Greenhouse influenced rivers had significantly higher concentrations of macronutrients (nitrogen, phosphorus, and potassium) and trace metals (copper, molybdenum, and zinc). Concentrations within greenhouse influenced rivers appeared to decrease over the 5-year study while concentrations within non-greenhouse influenced river remained constant. The different temporal pattern between river types was attributed to increased precipitation during the study period. Increases in precipitation diluted concentrations in greenhouse influenced rivers; however, non-influenced river runoff proportionally increased nutrient mobility and flow, stabilizing the observed concentrations of non-point sources. Understanding the dynamic nature of environmental releases of point and non-point sources of nutrients and trace metals in mixed agricultural systems using riverine water chemistry is complicated by changes in climatic conditions, highlighting the need for long-term monitoring of nutrients, river flows and weather data in assessing these agricultural sectors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".