Origin and Accumulation of an Anthropogenic CO<sub>2</sub> and <sup>13</sup>C Suess Effect in the Arctic Ocean
Bibliographic record
Abstract
Abstract We determined the impact of anthropogenic CO2 (Cant) accumulation on the δ13C of dissolved inorganic carbon in the Arctic Ocean (i.e., the 13C Suess effect) based on δ13C measurements during a GEOTRACES cruise in 2015. The δ13C decrease was estimated from the amount of Cant change derived by the transit time distribution approach and the ratio of the anthropogenic δ13C/dissolved inorganic carbon change (RC). A significant Cant increase (up to 45 μmol kg−1) and δ13C decrease (up to −0.9‰) extends to ~2,000 m in the Canada and Makarov Basin. We find distinctly different RC values for the intermediate water (300–2,000 m) and upper halocline water (<200 m) of −0.020 and −0.012‰ (μmol kg−1)−1, respectively, which identifies two sources of Cant accumulation from North Atlantic and North Pacific. Furthermore, estimated RC for intermediate waters is the same as the RC observed in the Greenland Sea and the rate of anthropogenic dissolved inorganic carbon increase estimated for intermediate waters at 0.9 μmol kg−1 yr−1 is identical to the estimated rate in the Iceland Sea. These observations indicate that the high rate of Cant accumulation and δ13C decrease in the Arctic Ocean is primarily a result of the input of Cant, via ventilation of intermediate waters, from the Nordic Sea rather than local anthropogenic CO2 uptake within the Arctic Basin. We determine the preindustrial δ13C (δ13CPI) distributions and find distinct δ13CPI signatures of the intermediate and upper halocline waters that reflect the difference in δ13CPI–PO4 relationship of Atlantic and Pacific source water.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".