EarthResourceML/INSPIRE Mineral Resources data models and ERML Lite: Data Standards to Deliver Mineral Resources Data EU and Globally
Bibliographic record
Abstract
EarthResourceML (http://www.earthresourceml.org) is the international data model and standard for mineral resources data. EarthResourceML (ERML) was initially developed for the Australian Chief Government Geologists Committee (CGGC) but is now under the governance of the Commission for Geoscience Information (CGI), a commission of the International Union of Geological Sciences (IUGS). ERML/INSPIRE MR data models are the preferred standard for mineral resources data sharing initiatives and projects, such as the European Union’s INSPIRE directive, and EU-projects (Minerals4EU, ProSUM) and the Australian AuScope, and Geoscience Portal projects. The current version of ERML v2.0 was released in August 2014 and ERML v3.0 will be published 2018. Current ERML v.2.0 and INSPIRE Mineral Resource data models are practically identical. The main elements of the ERML/INSPIRE models cover mineral occurrences, mines, and mining activity. The standard describes the geological characteristics and settings of mineral occurrences, their contained commodities, and their mineral reserve, resource and endowment. It is also able to describe mineral exploration, mines and mining activities, processing/transformation activities, with the production of concentrates and refined products, and waste material characterization. ERML/INSPIRE utilises the GeoSciML v4.1 Mapped Feature model to describe spatial representations of mineral occurrences and mines, and the GeoSciML Earth Material model to describe host- and associated materials. ERML Lite v. 1.0 version was accepted and released in August 2016. The new version 2.0 of ERML Lite was published June 2018. ERML Lite 2.0 delivers a user-friendly designed and simplified flat view of key elements of the full ERML/INSPIRE data models. The CGI Geoscience Terminology Working Group (http://resource.geosciml.org/def/voc/) and INSPIRE code list register (http://inspire.ec.europa.eu/codelist/) provides a range of standard vocabularies that can be used to populate ERML/INSPIRE data services. ERML Lite test service will be demonstrated at the AGU meeting at Onegeology portal and data providers are from Oceania (AUSGIN and New Zealand), Europe (Minerals4EU, FODD, and Finland) and Arctic (60°- 90°) data (Nordic Countries, Russia, Alaska, and Canada). The ERML and ERML Lite data models enable comparison of mineral resource information from different jurisdictions. With increasing participation from geological surveys these data model will assist global resources estimates and exploration targeting.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.054 |
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".