Numerical dye tracer experiments in Bedford Basin in support of Ocean Alkalinity Enhancement research
Bibliographic record
Abstract
Ocean Alkalinity Enhancement (OAE) is considered as a potential technique to mitigate ocean acidification and remove carbon dioxide (CO2) from the atmosphere. In this study, a suite of numerical tracer experiments was conducted using a high-resolution nested model to support ongoing OAE field trials in Halifax Harbor and Bedford Basin. We first estimated the residence time, which provides an overall description of the circulation, for different seasons over the past 20 years (2003-2022). Results show a clear seasonal pattern in residence time which is longest in July and shortest in January. Particles with different dissolution rates and sinking velocities were then added continuously through the cooling outfall of a local power plant for three months to simulate the dissolution, dispersion, and movement of different alkaline mineral feedstocks. To account for inter-annual variability, the years with the longest and shortest residence time in each season were selected to perform these simulations. Furthermore, tracer simulations will be compared with ongoing Rhodamine WT field trials. Results obtained thus far show that the surface alkalinity signal due to OAE is most likely to be detected near the cooling outfall but depends on the tidal stage and the local circulation and weather conditions. Detectability is highest in July because the residence time is longest. In addition, the detectability increases with faster dissolution rate and slower sinking velocity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".