The role of industry-academia collaborative research in mineral exploration
Bibliographic record
Abstract
ABSTRACT Many people may not be aware of the extent of Kurt Kyser's collaboration with mineral exploration companies through applied research and the development of innovative exploration technologies, starting at the University of Saskatchewan and continuing through the Queen's Facility for Isotope Research. Applied collaborative, geoscientific, industry-academia research and development programs can yield technological innovations that can improve the mineral exploration discovery rates of economic mineral deposits. Alliances between exploration geoscientists and geoscientific researchers can benefit both parties, contributing to the pure and applied geoscientific knowledge base and the development of innovations in mineral exploration technology. Through a collaboration that spanned over three decades, we gained insight into the potential for economic uranium deposits around the world in Canada, Australia, USA, Finland, Russia, Gabon, Namibia, Botswana, South Africa, and Guyana. Kurt, his research team, postdoctoral fellows, and students developed technological innovations related to holistic basin analysis for economic mineral potential, isotopes in mineral exploration, and biogeochemical exploration, among others. In this paper, the business of mineral exploration is briefly described, and some examples of industry-academic collaboration innovations brought forward through Kurt's research are identified. Kurt was a masterful and capable knowledge broker, which is a key criterion for bringing new technologies to application—a grand, curious, credible, patient, and attentive communicator—whether talking about science, business, or life and with first ministers, senior technocrats, peers, board members, first nation peoples, exploration geologists, investors, students, citizens, or friends.
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.
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 | 0.083 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.029 |
| Scholarly communication | 0.030 | 0.018 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".