Carbon Capture, Utilization, and Storage in Newfoundland and Labrador: A Simple Analysis of Viability of CCUS Systems and Technologies in the Canadian Province of Newfoundland and Labrador
Bibliographic record
Abstract
Carbon Capture, Utilization and Storage (CCUS) is the only group of technologies that is known to decrease the amount of CO2 in the Earth’s biosphere, leading some experts to hail it as the cure for climate change. A significant number of CCUS projects have been implemented across the world and are being considered by many governments and policymakers, including the province of Newfoundland and Labrador. However, some are rightly skeptical of its perceived role as a cure for climate change. Through analyses of different CCUS technologies, the province’s needs and resources, the risks associated with CCUS, and the economics and politics of CCUS in the province, we have concluded that Newfoundland and Labrador possess not just the capability of CCUS, but the potential of a major sequestration project. We highlight the need for more data to support simulations and modeling for more accurate assessment of specific CCUS implementation in Newfoundland and Labrador, as well as funding and incentives for emitters to participate in CCUS – in short, it is imperative that Newfoundland and Labrador steeply accelerate its planning, simulation, development, and implementation of CCUS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".