A Canada-Wide Ocean Biogeochemical Model Encompassing the North Atlantic, North Pacific and Arctic Oceans
Bibliographic record
Abstract
A numerical ocean model with biogeochemistry has been developed for a domain that spans oceans around Canada: extending to 26°N in the Atlantic and 44°N in the Pacific and including the whole Arctic Ocean. The spatial resolution is ∼0.25° in longitude/latitude with 75 vertical levels. A series of simulations was conducted to assess the best choices for biogeochemical model parameters across the diverse regions, using a variety of validation data sets including satellite ocean colour (surface chlorophyll and particulate organic carbon, integrated primary production), surface underway pCO2, and depth profiles of oxygen and nitrate concentration from ships and Argo floats. In addition to parameter values, processes examined include interactive sediments, fluvial nutrients, light attenuation by fluvial coloured dissolved organic matter (CDOM), and iron limitation. The results indicate that the optimal parameter set is one that limits phytoplankton losses to grazing and other processes so as to ensure strong biological drawdown of dissolved inorganic carbon and nutrients in spring and summer; both insufficient and excessive drawdown were observed. Sensitivity to other processes such as interactive sediments, fluvial nutrients or CDOM attenuation was weak in most regions. Fluvial nutrients can cause localized reduction of pCO2 by as much as 60 μatm, and attenuation by CDOM or sequestration of nutrients in the sediment can reduce primary production and zooplankton biomass in regions with large river inputs or broad continental shelves. Iron limitation has a non-negligible effect on the model solution even in regions generally considered iron-replete; a model that successfully spans iron-limited and non-iron-limited domains will require complete and accurate specification of iron sources and sinks.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".