Distinct sources of uncertainty in simulations of the ocean biological carbon pump at different depths
Bibliographic record
Abstract
Abstract Model uncertainty in simulating the biological carbon pump was quantified and partitioned using 14 models from the Coupled Model Intercomparison Project Phase 6. Uncertainty increases with depth. On the global scale, uncertainty in carbon export dominates above 900 m and uncertainty in transfer efficiency below. Reducing model uncertainty in carbon export and transfer efficiency offers similar benefits for understanding century-scale carbon sequestration and climate. These models produce three different qualitative patterns in transfer efficiency: one where it is globally homogenous and two opposite latitudinal patterns due to different model structures and parameters. The exponent b of the Martin curve, which has long been used to compare different representations of transfer efficiency, is shown here to underestimate uncertainty in transfer efficiency. This highlights the importance of using vertical profiles of carbon flux rather than the single exponent b in model validation and intercomparison exercises.
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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 | 0.000 | 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 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".