1Mapping Groundwater Pollution Risk within an Agricultural Watershed Using Modeling,
Bibliographic record
Abstract
With the objective of studying the atrazine transport using all the available soil attributes as input for a model and to predict the risk of groundwater pollution within a region which contributes to the Great Lakes, two study areas were selected for Southern Ontario: the Grand River Watershed at 1:1,000,000. The polygon information was obtained from the National Soil Data Base (NSDB). Using the soil codes and modifiers as a key, the soil attributes needed were extracted from the Soil Layer Files (SLF), for three layers. From the 119 polygons within the window, many attributes were missing, for which values were estimated using pedotransfer functions available from the literature. The pedotransfer functions were tested against field measured data and their performance was evaluated through the value of the linear correlation coefficient. The assessment of groundwater contamination with atrazine was done through the calculation of the annual mass loading and the time necessary for it to reach 3 ppb at 90 cm depth, through a water and solute transport model called LEACHM. The input data for the LEACHM model requires that for each soil profile data set a corresponding set of clima ological attributes should be provided. For that purpose, the climatological data corresponding to the
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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 | 0.001 | 0.001 |
| 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".