GroundWater Markup Language (GWML) – enabling groundwater data interoperability in spatial data infrastructures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it