Groundwater phosphorus concentrations: global trends and links with agricultural and oil and gas activities
Bibliographic record
Abstract
Although observations show that anthropogenic phosphorus (P) can reach groundwater supplies, there has been no comprehensive evaluation of P in groundwater at the global scale. Additionally, there have been minimal studies on distributed sources, such as agriculture, and the effects of oil and gas activities on P contamination in groundwater are poorly understood. We compile and analyze 181,653 groundwater P concentrations from 13 government agencies and 8 individual research studies in 11 different countries in order to determine the extent of P pollution at the global scale. We find that every country with data has groundwater P concentrations that pose a significant risk of eutrophication to surface waters. In Canada and the United States, we study the relationship between land use, focusing on crop/pastureland, and increased P concentrations in groundwater. In Ontario and Alberta, two Canadian provinces with different histories of oil and gas development, we find areas with a high concentration of P groundwater pollution to coincide with regions of intense oil and gas activity. Understanding the effects of anthropogenic sources on phosphorus contamination of groundwater and identifying all possible pathways through which contamination can occur will assist regulators in planning and implementing effective strategies to manage groundwater and surface water quality and sustain ecosystem health.
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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 | 0.002 | 0.007 |
| 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".