Paradigmatic Shifts in the Uranium Exploration Process: Knowledge Brokers and the Athabasca Basin Learning Curve
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Uranium exploration increased over the past decade in response to an increase in the price of uranium, with more than 900 companies engaged in the global exploration on over 3,000 projects. Major economic discoveries of new uranium orebodies have been elusive despite global exploration expenditures of $3.2 billion USD, with most of the effort in historical uranium districts. The increased effort in exploration with minimal return can be described through the example of a cyclical model based on exploration and discovery in the prolific Athabasca Basin, Saskatchewan. The model incorporates exploration expenditure, quantities of discovered uranium, and the sequence of uranium deposit discoveries to reveal that discovery cycles are epochal in nature and that they are also intimately related to the development and deployment of new exploration technologies. Exploration in the Athabasca Basin can be divided into an early “prospector” phase and the current “model-driven”phase. The future of successful uranium exploration is envisaged as the “innovation exploration” stage in which a paradigmatic shift in the exploration approach will take the industry towards new discoveries by leveraging research and technology development. Effective engagement within the “innovation exploration” paradigm requires that exploration organizations recognize knowledge brokers, and adopt research, development, and technology transfer as a long-term, systematic strategy, including critical definition of exploration targets, identification of innovation frontiers needed, enhanced leadership to accurately portray the research and development imperative and elevation of the status of the research and development effort within the organizational system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it