Potato Land Use and Nitrate Runoff Characteristics of Two Subcatchments of the Wilmot River Watershed, Prince Edward Island (PEI), Canada
Bibliographic record
Abstract
Abstract Two subcatchments in the upper reaches of an arable watershed in central Prince Edward Island (PEI), farmed mostly to potatoes (in rotation), were monitored year-round for nitrate runoff. Land management inventories were done every fall and spring and assessed against nitrate runoff through regression analysis for the period 1991 to 2004. Nitrate concentration (averaging approximately 7 mg L-1 over 14 years) in the outflow varied considerably yearly (standard deviation: 2.76) and monthly (standard deviation: 3.43), and exceeded quality guidelines (of 13 mg L-1) for aquatic life in freshwaters at about a 6% frequency, averaging a nitrate-dollar loss of $1.70 per hectare per year.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".