Assessment of pathogen pollution in watersheds using object-oriented modeling and probabilistic analysis
Bibliographic record
Abstract
A limited number of research trials have been reported in the past to model pathogenic organisms in streams and large water bodies at a watershed scale. In this paper, modeling of fecal coliform in streams is proposed from a management perspective at the watershed level. To model the fate and transport of fecal coliform in a watershed in Southeastern Kentucky, an object-oriented (OO) simulation model, based on the concepts of system dynamics (SD) approach, is proposed in this study. The approach combines both data-driven approaches and insights gained from a process-based approach. Different management scenarios, based on flow conditions and pollution sources, are generated and evaluated to validate the proposed approach. Deterministic and conceptually simple probabilistic analyses are carried out to understand several water quality management alternatives that aim to reduce pollutant loadings. Results point to the potential use of the proposed OO–SD framework in addressing environmental policy issues and also to the need for relying on probabilistic analysis to obtain more credible results and recommendations in data-poor conditions. The proposed approach helps direct limited funding and watershed management efforts to be focused on areas that have the greatest impact on the surface water quality conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".