Evaluating best management practices for agricultural watersheds using probabilistic models
Bibliographic record
Abstract
This research describes an approach to organize results from a non-point source (NPS) pollution model into a framework for analysis and decision making. A review of NPS models and a survey of techniques used for constructing decision-support software systems is given. The NPS model selected for this investigation was the Guelph model for evaluating effects of Agricultural Management systems on Erosion and Sedimentation (GAMES). The organizational framework consists of a graphical user interface that interacts with a probabilistic model. The probability model integrates probability distributions of important variables and relationships between variables from model simulations. The graphical probability model consists of a network of nodes that represent individual parameters or variables for fields in a watershed. Directed links indicate relationships between variables. The complete graph thus has directed links that follow the drainage network of the watershed. The relationships (or links) between the nodes are quantified by way of data derived by Monte Carlo simulation of the GAMES model and by deterministic functions as specified in GAMES. Probability models were developed for two Southern Ontario watersheds to demonstrate how the system can be used for targeting best management practices. With this technique, it is possible to select any configuration of management practices in the watershed in order to obtain estimates of erosion rates and sediment yield without the need to return to the original simulation model.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".