Carbon Storage Potential in Offshore Nova Scotia, Canada
Bibliographic record
Abstract
Summary Carbon management is a growing global industry that is critical to achieving emissions reduction targets while presenting new economic opportunities. Atlantic Canada is well-positioned to lead in this field through its offshore geologic storage resources and an emerging marine carbon dioxide removal (mCDR) sector. With a relatively small emissions profile and significant storage potential, the region could serve as a carbon sink for national and international efforts. Realizing this potential requires coordinated development and further geological and marine research. Net Zero Atlantic is advancing this work by leading technical studies and supporting initiatives. The Nova Scotia Offshore CCS Atlas is refining earlier estimates of carbon storage potential on the Scotian Shelf to support future project planning. This includes geologic and reservoir engineering analyses, probabilistic assessments, and digital models, with publication planned for 2027. Concurrent mCDR research, including Ocean and River Alkalinity Enhancement, is progressing through academic and start-up collaborations. Insights from a 2024 CCS Roundtable have informed the development of a regional Roadmap outlining key actions to enable CCS deployment. With storage potential exceeding local needs, Atlantic Canada has the potential to become a carbon management hub serving eastern Canada, the U.S., and Europe.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".