Trends of Anthropogenic Dissolved Inorganic Carbon in the Northwest Atlantic Ocean Estimated Using a State Space Model
Bibliographic record
Abstract
Abstract The northwest Atlantic Ocean is an important sink for carbon dioxide produced by anthropogenic activities. However the strong seasonal variability in the surface waters paired with the sparse and summer biased observations of ocean carbon makes it difficult to capture a full picture of its temporal variations throughout the water column. We aim to improve the estimation of temporal trends of dissolved inorganic carbon (DIC) due to anthropogenic sources using a new statistical approach: a time series generalization of the extended multiple linear regression (eMLR) method. Anthropogenic increase of northwest Atlantic DIC in the surface waters is hard to quantify due to the strong, natural seasonal variations of DIC. We address this by separating DIC into its seasonal, natural and anthropogenic components. Ocean carbon data is often collected in the summer, creating a summer bias, however using monthly averaged data made our results less susceptible to the strong summer bias in the available data. Variations in waters below 1000m have usually been analyzed on decadal time scales, but our monthly analysis showed the anthropogenic carbon component had a sudden change in 2000 from stationary to an increasing trend at the same rate as the waters above. All depths layers had similar rates of anthropogenic increase of ∼0.57µmol kg−1 year−1, and our uncertainty levels are smaller than with eMLR results. Integration throughout the water column (0–3,500 m) gives an anthropogenic carbon storage rate of 1.37 ± 0.57 mol m−2 year−1, which is consistent with other published estimates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".