Ventilation of the Arctic Ocean: Mean ages and inventories of anthropogenic CO<sub>2</sub> and CFC‐11
Bibliographic record
Abstract
The Arctic Ocean constitutes a large body of water that is still relatively poorly surveyed because of logistical difficulties, although the importance of the Arctic Ocean for global circulation and climate is widely recognized. For instance, the concentration and inventory of anthropogenic CO2 (Cant) in the Arctic Ocean are not properly known despite its relatively large volume of well‐ventilated waters. In this work, we have synthesized available transient tracer measurements (e.g., CFCs and SF6) made during more than two decades by the authors. The tracer data are used to estimate the ventilation of the Arctic Ocean, to infer deep‐water pathways, and to estimate the Arctic Ocean inventory of Cant. For these calculations, we used the transit time distribution (TTD) concept that makes tracer measurements collected over several decades comparable with each other. The bottom water in the Arctic Ocean has CFC values close to the detection limit, with somewhat higher values in the Eurasian Basin. The ventilation time for the intermediate water column is shorter in the Eurasian Basin (∼200 years) than in the Canadian Basin (∼300 years). We calculate the Arctic Ocean Cant inventory range to be 2.5 to 3.3 Pg‐C, normalized to 2005, i.e., ∼2% of the global ocean Cant inventory despite being composed of only ∼1% of the global ocean volume. In a similar fashion, we use the TTD field to calculate the Arctic Ocean inventory of CFC‐11 to be 26.2 ± 2.6 × 106 moles for year 1994, which is ∼5% of the global ocean CFC‐11 inventory.
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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 | 0.001 | 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 | 0.000 |
| 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".