Modelling of ocean circulation in theNewfoundland Basin
Bibliographic record
Abstract
The average temperature of the Earth's surface has increased significantly since preindustrial levels and continues to rise. This global warming is caused by the increased level of greenhouse gases in the atmosphere, particularly carbon dioxide (CO₂). To reduce the rate of temperature increase, it is necessary to decrease the amount of CO₂ in the atmosphere. One way to achieve this is by allowing oceans to absorb more CO₂ through a process known as ocean alkalinity enhancement (OAE), which occurs when alkaline particles are added to the ocean surface. The observational tracking of particle trajectories through the ocean presents significant challenges; therefore, ocean models are primary tools for predicting particle trajectories. This research focuses on ocean modelling around the Newfoundland Basin. The Regional Ocean Modelling System (ROMS) has been implemented for this region. ROMS is a hydrostatic, free-surface ocean model which uses a terrain-following vertical coordinate. The continuous model equations of ROMS, along with their numerical implementations, are described. The model initialization, as well as the definition of surface forcing and boundary conditions, are presented. The method of particle tracking in ROMS is described. Preliminary results from model simulations of ocean characteristics, including ocean temperature, salinity, and sea surface height, for July 2020 are outlined. Particle trajectories after a month of circulation within the Newfoundland Basin are shown. Future work, including extended model duration and different particle distribution methods, is then discussed in the context of applications to OAE.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".