The changing role of Geological Surveys: introduction
Bibliographic record
Abstract
Abstract This volume is a collection of papers authored by senior managers and heads of Geological Survey organizations (GSOs) from around the world in an attempt to provide a benchmark on how GSOs are responding to national and international needs in a rapidly changing world. GSOs face an uncertain future and need to understand global trends. Whereas population trends are somewhat predictable, societal responses to change are much less so and technological change is fundamentally disruptive and chaotic. As countries adopt sustainable development principles and the public becomes increasingly (but not necessarily reliably) informed about environmental issues using social media, the integration of resource development and environmental stewardship becomes increasingly important. GSOs will continue to provide key information about Earth systems, natural hazards and climate change in this context. This introduction comprises a short review of the global trends affecting GSOs, a snapshot of the state of GSOs, examples of how GSOs are adapting their activities to the modern world, including the growing use of big data, and an examination of international collaboration between GSOs. The time is perhaps ripe to reinforce international collaborations through a global network of GSOs. To achieve this will require leadership and a focus on the big picture of global sustainability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".