Macrostrat: A Platform for Geological Data Integration and Deep‐Time Earth Crust Research
Bibliographic record
Abstract
Abstract Characterizing the lithology, age, and physical‐chemical properties of rocks and sediments in the Earth's upper crust is necessary to fully assess energy, water, and mineral resources and to address many fundamental questions. Although a large number of geological maps, regional geological syntheses, and sample‐based measurements have been produced, there is no openly available database that integrates rock record‐derived data, while also facilitating large‐scale, quantitative characterization of the volume, age, and material properties of the upper crust. Here we describe Macrostrat, a relational geospatial database and supporting cyberinfrastructure that is designed to enable quantitative spatial and geochronological analyses of the entire assemblage of surface and subsurface sedimentary, igneous, and metamorphic rocks. Macrostrat contains general, comprehensive summaries of the age and properties of 33,903 lithologically and chronologically defined geological units distributed across 1,474 regions in North and South America, the Caribbean, New Zealand, and the deep sea. Sample‐derived data, including fossil occurrences in the Paleobiology Database, more than 180,000 geochemical and outcrop‐derived measurements, and more than 2.3 million bedrock geologic map units from over 200 map sources, are linked to specific Macrostrat units and/or lithologies. Macrostrat has generated numerous quantitative results and its infrastructure is used as a data platform in several independently developed mobile applications. It is necessary to expand geographic coverage and to refine age models and material properties to arrive at a more precise characterization of the upper crust globally and test fundamental hypotheses about the long‐term evolution of Earth systems.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".