Planetary Technologies' Groundbreaking Marine Carbon Dioxide Removal (mCDR) Project in Halifax, and the Emergence of Halifax as a Global mCDR Hub
Bibliographic record
Abstract
Ocean Alkalinity Enhancement (OAE) is a marine carbon dioxide removal (mCDR) pathway that is widely accepted to be both permanent in its CO2 storage and, potentially, the most scalable pathway available today. Here, we outline the science behind OAE, and provide details of the past and present advances in the science, engineering, and community building at a worlds-first OAE field trial site in Halifax, Nova Scotia. The site is operated by Halifax-based Planetary Technologies, and the R&D work is conducted alongside numerous local and international collaborators spanning academia, philanthropic organizations, and engineering firms. While the fundamental science is relatively straightforward, the practical deployment of Planetary's end-to-end OAE process is complex and interdisciplinary, and includes the following elements: site scoping and development, the engineered mechanism of adding alkaline materials to the ocean, and the eventual generation of high-quality carbon credits. Workstreams include permitting and regulatory compliance, meaningful community engagement and governance, established protocols to ensure operational, environmental, and social safety, oceanographic sensing using traditional boat-based and novel autonomous platforms, spatiotemporal ocean modelling at scales spanning multiple orders of magnitude, and techniques for both quantifying uncertainties and applying those uncertainties to ensure high-confidence and thus high-quality credits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".