ATES: a geo-informatics decision aid tool for the integration of groundwater into land planning
Bibliographic record
Abstract
Groundwater is the primary source of drinking water for small municipalities and individuals. However, groundwater can be polluted by almost any land use. Consequently, many governments have acquired groundwater information in the aim of protecting the resource. Nevertheless, the resulting data are often ill-fitted to planning needs. In a previous study, a method was developed to help planners interpret hydrogeological data. It combines land planning and hydrogeological data through multicriteria analysis, in order to obtain groundwater contamination risk maps. The method proved efficient and useful. However, it could not be easily implemented by land planners, who do not always have training with these types of data and geographical information system (GIS). This paper presents how the method was integrated into a web-based interface called Aménagement du Territoire et Eau Souterraine (ATES). ATES allows planners to view groundwater basic maps, evaluate the present contamination risk for groundwater, and analyse new planning scenarios. ATES also suggests mitigation measures and offers tools to discuss the possible solutions. The tool has been developed, tested and validated with land planners. To our knowledge, it is the first geo-informatics tool developed especially for planners that aims at facilitating the incorporation of groundwater into planning. Moreover, an innovative approach called MACBETH was used for data aggregation, a novelty in groundwater management and spatial data integration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".