Using Noble Gases to Assess the Ocean's Carbon Pumps
Bibliographic record
Abstract
Natural mechanisms in the ocean, both physical and biological, concentrate carbon in the deep ocean, resulting in lower atmospheric carbon dioxide. The signals of these carbon pumps overlap to create the observed carbon distribution in the ocean, making the individual impact of each pump difficult to disentangle. Noble gases have the potential to directly quantify the physical carbon solubility pump and to indirectly improve estimates of the biological organic carbon pump. Noble gases are biologically inert, can be precisely measured, and span a range of physical properties. We present dissolved neon, argon, and krypton data spanning the Atlantic, Southern, Pacific, and Arctic Oceans. Comparisons between deep-ocean observations and models of varying complexity enable the rates of processes that control the carbon solubility pump to be quantified and thus provide an important metric for ocean model skill. Noble gases also provide a powerful means of assessing air-sea gas exchange parameterizations.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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 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".