Modeling <i>p</i> CO <sub>2</sub> variability in the Gulf of Mexico
Bibliographic record
Abstract
Abstract. A three-dimensional coupled physical–biogeochemical model was used to simulate and examine temporal and spatial variability of sea surface pCO2 in the Gulf of Mexico (GoM). The model was driven by realistic atmospheric forcing, open boundary conditions from a data-assimilative global ocean circulation model, and observed freshwater and terrestrial nutrient and carbon input from major rivers. A 7-year model hindcast (2004–2010) was performed and validated against ship measurements. Model results revealed clear seasonality in surface pCO2 and were used to estimate carbon budgets in the Gulf. Based on the average of model simulations, the GoM was a net CO2 sink with a flux of 1.11 ± 0.84 × 1012 mol C yr−1, which, together with the enormous fluvial inorganic carbon input, was comparable to the inorganic carbon export through the Loop Current. Two model sensitivity experiments were performed: one without biological sources and sinks and the other using river input from the 1904–1910 period as simulated by the Dynamic Land Ecosystem Model (DLEM). It was found that biological uptake was the primary driver making GoM an overall CO2 sink and that the carbon flux in the northern GoM was very susceptible to changes in river forcing. Large uncertainties in model simulations warrant further process-based investigations.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".