A Framework for Mineral Geoscience Data and Model Portability
Bibliographic record
Abstract
We have developed a data structure called GEOH5 with the objective of integration and storage of geological models, data, and metadata where dissemination, ease of access, and persistence are required without commercial encumbrance. Our emphasis is on the needs of the mineral industry which, unlike the upstream oil and gas industry, otherwise lacks common data exchange formats with a scope encompassing most exploration and production data types. Although only a few years old, the data structure is already in use by many thousands of users with increasing acceptance across mineral geoscience and engineering. This includes industry, academia, and geological surveys that use GEOH5 as a documented, public, easy-to-use, vendor-neutral, and permanently accessible means of storage and communication. GEOH5 is open source and free to use. It is based on open-source HDF5 technology because of its many advantages: wide acceptance across numerous data-intensive industries, self-describing behaviour through integration of data and metadata, fast I/O, excellent compression, file merging, cross-platform capability, unlimited data size, and access to libraries in a variety of programming languages. It provides professionals, researchers, and the public at large with a robust means of managing, exchanging, and visualising large quantities of diverse mineral geoscience and engineering data.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".