Controls on Nitrate Loading and Implications for <scp>BMPs</scp> Under Intensive Potato Production Systems in Prince Edward Island, Canada
Bibliographic record
Abstract
Abstract Potato production is critical to economy of Prince Edward Island ( PEI ), but has been linked to increasing groundwater nitrate contamination and anoxic events in estuaries. Given that PEI is entirely groundwater‐dependent for potable water, this has resulted in considerable pressure to find solutions to protect water quality. However, our understanding of the controls on nitrate loading to groundwater, and the consequent potential to mitigate nitrate loading through beneficial management practices ( BMPs ), is limited. In PEI , flow and nitrate transport in the vadose zone are controlled by the matrix porosity of the till and sandstone, while flow and transport in the saturated zone are controlled by a conductive fracture network with limited storage capacity. Diffusion of nitrate stored in the matrix may introduce a significant time lag between reduced nitrate loading from implementation of BMPs and measureable improvement in groundwater quality. BMPs with potential to mitigate nitrate loading to groundwater through improved nitrogen fertilizer practices in producing potato crops have been identified, but implementation of these practices alone is not sufficient to control nitrate loading due to the economic context of potato production. Additional BMPs for improved potato cropping systems have also been identified; however, reducing the intensity of agricultural production, including the proportion of land in potato production, at the watershed level may be required to meet the drinking water guideline for nitrate in PEI . Given the important role of market forces in determining profitability of crop production, it becomes challenging to modify crop production practices to meet an environmental goal without some additional external incentive or public pressure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".