High‐Resolution Neural Network Demonstrates Strong CO <sub>2</sub> Source‐Sink Juxtaposition in the Coastal Zone
Bibliographic record
Abstract
Abstract The role of coastal oceans in regulating atmospheric carbon dioxide remains poorly quantified and understood. Here, we use a two‐step neural network approach to generate estimates from sparse observational data in the coastal Northeast Pacific Ocean at an unprecedented spatial resolution of 1/12° with coverage in the nearshore (0–25 km offshore). We compiled partial pressure of carbon dioxide ( p CO 2 ) observations as well as a range of predictor variables including satellite‐based and physical oceanographic reanalysis products. With the predictor variables representing processes affecting p CO 2 , we created non‐linear relationships to interpolate observations from 1998 to 2019. Compared to in situ shipboard and mooring observations, our coastal p CO 2 product captures broad spatial patterns and seasonal cycle variability well. A sensitivity analysis identifies that the parameters responsible for the neural network's ability to capture regional p CO 2 variability are associated with mechanistic processes, including mixed layer deepening, mesoscale eddies, and gyre upwelling. Using wind speed and atmospheric CO 2 , we calculated air‐sea CO 2 fluxes. We report an anticorrelation between annual air‐sea CO 2 flux and its seasonal amplitude with the relationship driven by circulation, opposing seasonal upwelling/relaxation versus downwelling, and the effects of winter mixing and primary productivity. We show that the inclusion of nearshore net outgassing fluxes lowers the overall regional net flux. Overall, our results suggest that the region is a net sink (−0.7 mol m −2 yr −1 ) for atmospheric CO 2 with trends indicating increasing oceanic uptake due to strong connectivity to subsurface waters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".