Bibliographic record
Abstract
Groundwater is the safest and most reliable source of available freshwater. Although traditionally groundwater has been assumed to be free from contamination, numerous discoveries, in recent years of toxic chemicals in well water have proven this assumption to be false. Groundwater contamination from chemical dump sites tends to attract the greatest public attention, but contamination from other sources such as septic systems, pesticides, and underground storage tanks also can be significant. Intensive agriculture in areas of high soil permeability and high water tables also causes groundwater contamination from the percolation of chemicals and nutrients through the soil profile. Protecting groundwater resources from pollution is therefore essential for its proper management and preventing probable hazards. Groundwater vulnerablility assessment is an issue of spatial distribution and therefore typically carried out using geographic information systems (GIS). Even when using a simple qualitative method, the complex processing of spatial information is completed faster using GIS Models are tools to simulate the behavior of physical systems. They can predict the future evolution of the systems, they can be used as interpretative tools in order to study system dynamics and they can give hints for data collection and design of experiments. Models are sometimes used to examine the evolution of generic natural systems, without a specific application to a definite site or population. ArcGis 9 provides new tolls to build protection model to study grounwater contamination isues of various watersheds that performs multiple geoprocessing operations. The study articulates the most vulnerable locations of Niagara for groundwater contamination, what geospatial data are needed to support these resource assessment activities, and how GIS tools are required to facilitate the generation of a best optimized model.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".