A high-resolution nested model to study the effects of alkalinity additions in a mid-latitude coastal fjord
Bibliographic record
Abstract
Surface ocean alkalinity enhancement (OAE), through the release of alkaline materials, is an emerging carbon dioxide removal (CDR) technology that could increase the storage of anthropogenic carbon in the ocean. Although essential, evaluating the effects of alkalinity additions on the carbonate system and ultimately on air-sea CO2 fluxes is not straight forward. Observations, even with autonomous platforms, are inherently sparse and limited, and therefore cannot provide a comprehensive quantification of the effects of OAE. Numerical models are important complementary tools. They can help guide fieldwork design, provide forecasts of the ocean state, and simulate the effects of alkalinity additions on the seawater carbonate system. Here we describe a coupled physical-biogeochemical implementation of ROMS in a nested grid configuration that reaches a very high spatial resolution in Bedford Basin (51m), a coastal fjord in eastern Canada that is chosen as a test site for OAE. The biogeochemical model simulates oxygen dynamics and the carbonate system, including air-sea gas exchange. We present a multi-year hindcast validated against the long-term weekly time series available at the Compass Buoy station in the centre of the Basin as well as recent simulations carried out during alkalinity addition trials. We will discuss the model’s capabilities with respect to OAE and the challenges ahead.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".