GroundWater Markup Language (GWML) – enabling groundwater data interoperability in spatial data infrastructures
Bibliographic record
Abstract
Increasing stress on global groundwater resources is leading to new approaches to the management and delivery of groundwater data. These approaches include the deployment of a Spatial Data Infrastructure (SDI) to enable online data interoperability amongst numerous and heterogeneous data sources. Often an important component of an SDI is a global domain schema, which serves as a central structure for the query and transport of data, but at present there does not exist a schema for groundwater data that is strongly compliant with SDI concepts, standards, and technologies. In this paper we present GroundWater Markup Language (GWML), a groundwater application of the Geography Markup Language (GML). GWML can be used in conjunction with a variety of web services to facilitate data interoperability in a SDI. We describe three common usage scenarios that motivate the design of GWML and a three-stage design methodology involving conceptual, logical and physical schemas. The resultant GWML has broad scope as demonstrated by its implementation in the Canadian Groundwater Information Network. Example uses include decision support in resource management, a scientific application for aquifer mapping, and a commercial application for drill site selection. These demonstrated uses suggest GWML can play a key role in emerging groundwater SDI.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".