Monitoring and simulation of nutrient transport from agricultural fields
Bibliographic record
Abstract
In the Missisquoi Bay of Lake Champlain situated in the South of Quebec, phosphorus originating from agricultural sources has been found to be a major contributor to the deterioration of water quality. This study sought to evaluate the nutrient loads, most particularly phosphorus, exported through surface runoff and tile drainage from two agricultural fields of the Missisquoi Bay watershed. As part of the study, a phosphorus simulation model was tested on one agricultural field. The evaluation of FHANTM 2.0 assessed the model's capacity to simulate the transport of phosphorus on agricultural fields. From the two experimental fields studied, the results showed that the mean phosphorus load exported was larger in surface runoff than in tile drainage. The mean phosphorus load exported was 1.21 kg ha-1yr -1 in surface runoff, and 0.61 kg ha-1yr-1 in tile drainage. In contrast, nitrate loads exiting the fields were larger in tile drainage than in surface runoff. Over the two year study, the mean nitrate load was 5.64 kg ha-1yr-1 in surface runoff, and 91.43 kg ha-1yr-1 in tile drainage. FHANTM's simulation of hydrology for one field gave slightly negative coefficients of performance (CP), representing a poor capacity to simulate surface and subsurface runoff depths. The simulation of phosphorus concentrations in surface runoff showed a small range of values compared to field measurements, while simulations of phosphorus concentration in tile drainage were considered acceptable. Therefore, the overall evaluation of the FHANTM 2.0 model indicated that it had difficulty in simulating the transport of phosphorus from an agricultural field in Quebec.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".