Bibliographic record
Abstract
The intensification of economic development over the past 200 years and the increasing impact and conflict in land use requires an adaptation in how we investigate, analyze, report, store, and disseminate geological knowledge. In the 19th century William Smith mapped much of Great Britain in two dimensions. Smith’s mapping was spurred on by the emergence of the industrial revolution and enormous changes required to support mineral exploration, and transportation of raw and manufactured goods. In developed countries, particularly in Europe, there is an increasing realization and acknowledgement of the need for three-dimensional (3D) geological mapping programs to address the complexity of conflicting land use practices in the 21st century. Two hundred years after Smith’s seminal map, the 21st century requires a move from 2D to 3D geological mapping, particularly at the national scale (e.g., Thorleifson et al., 2010). This transition is less dramatic than it might seem. Even in the early maps of Smith, geology was presented with an appreciation for the third dimension, through the use of cross-sections and subsequently structural symbols. A wealth of subsurface information has been accumulated from drilling and geophysical studies supporting surface mapping. With access to surface and subsurface data, Geological Survey Organizations (GSOs) in a number of jurisdictions have made significant progress in the development and implementation of 3D mapping programs (e.g., Howard et al., 2009; Berg et al., 2011; Meulen et al., 2013; Mather et al., 2014). Such programs commonly have much broader objectives than just 3D visualization of a jurisdiction’s geology. These programs are focused on a full continuum of data management, storage, analysis and classification for 3D realization (e.g., Howard et al., 2009). The recent proliferation in jurisdictional wide 3D mapping is the outcome of the maturity of a digital transformation in geological data collection and management that started over 25 years ago and includes computer hardware and software developments of the past half century.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".