Subsurface Energy Transition Projects in Nova Scotia: Government-University-Industry Collaborations and Eage Student Competitions
Bibliographic record
Abstract
Summary Energy transition projects at Dalhousie University and the Nova Scotia DNRR (Department of Natural Resources and Renewables) have benefited from collaborations between industry, academia, and government. Regional studies and screening studies by ExxonMobil, GSC, DNRR, CNSOPB and OERA provided foundations for recent EAGE student competitions that have added innovation and multiple full-cycle realisations (geoscience-engineering-economics-HSE). Since 2021 ∼60 multi-disciplinary teams have participated in three Minus CO2 Challenges tackling complex offshore and onshore Nova Scotia projects. The 2021 competition ( First Break, April 2022 ) provided the first published quantitative assessment of carbon storage in deep saline aquifers on the Mesozoic Scotian Shelf demonstrating volumes similar to the North Sea. The 2022 competition ( First Break, June 2023 ) focussed on risking and success-case carbon-neutral development of light-oil prospects at Penobscot near Sable Island. In 2023, teams were charged with developing 300 MW of renewable energy, balancing load with CAES or hydrogen storage in Carboniferous salt caverns in the onshore Cumberland Basin. Modelling of geothermal energy in Cumberland Basin reflects the 2023 Laurie Dake Challenge (geothermal evaluation in the Vienna Basin, courtesy OMV).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".