Recent research in support of a low-carbon economy at the Geological Survey of Canada - Atlantic
Bibliographic record
Abstract
The governments of Canada and Nova Scotia have committed to a net-zero carbon budget by 2050. Achieving this goal would require rapid decarbonization of electrical generation as well as efforts to capture and sequester emitted carbon. Plans toward the former include offshore wind energy. Provincially, a target of 5 GW of offshore wind projects are to be awarded leases by 2030, while federally, a Regional Assessment for offshore wind energy on the continental shelves of Newfoundland and Nova Scotia is underway. Future projects will likely go through the impact assessment process, and key data informing these assessments will be geological due to the scale of seabed infrastructure associated with offshore wind energy production. Opportunities for geologic carbon sequestration are significant in the offshore Scotian Basin, especially in the Sable-Subbasin. Subsurface carbon storage development should be assessed for development in the same areas offshore wind projects are being considered due to the potential for stacked resource development. Both carbon storage and offshore wind will require infrastructure, and by identifying locations where they can be co-located, it could be possible to reduce environmental disturbance. The Geological Survey of Canada has begun data collection to fill some of the knowledge gaps around these key decarbonization efforts. This includes a recently completed mapping expedition (2024001 aboard the RV Coriolis II).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".