GeoSciML: Enabling the Exchange of Geological Map Data
Bibliographic record
Abstract
The CGI data model working group have established an initial geology data model and XML based exchange language to accommodate geological map data, referred to as GeoSciML. The language is based on prior work carried out at North American, European and Australian geological survey and research organisations. Unified Modelling Language (UML) has been used as a design aid for capturing the geological concepts and their properties. The UML model has then been converted to the GML-conformant GeoSciML.The design of GeoSCiML meets the short-term goal of accommodating the geoscience information presented on geological maps, as well as being fully extensible to include the full range of geological concepts covered by the geosciences. To demonstrate the ability of GeoSciML to deliver data via web feature services, a small subset has been selected as a testbed. This testbed will deliver lithostratigraphic units, boreholes, faults, contacts and compound materials from different national geological surveys.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".