Bibliographic record
Abstract
This study uses an existing perturbed parameter ensemble (PPE) of simulated ocean CO2 removal (CDR) to better determine sustainable pathways of ocean-based NET deployment and to provide information to constrain the design of subsequent modelling experiments. The results show that ocean alkalinity enhancement (OAE) can only help meet SDG13 (Climate Action) when other ambitious mitigation efforts are taken. This reinforces that OAE is not a substitute for emissions reduction, but could contribute to meeting our climate goals (if other factors suggest OAE is worth doing). For SDG14 (Life Below Water), the results suggest OEA can contribute to limiting or even reversing ocean acidification. Meeting many other SDG14 objectives is closely linked to also meeting SDG13. A key recommendation is therefore, that subsequent simulations in OceanNETs should only use SDG13 compatible baseline scenarios, unless there is some specific need for process understanding at higher levels of climate change. The analysis has also determined that the idealized CDR in the PPE is not suitable for determining many socio-economic constraints and the implications that these have for meeting the SDGs. Another key recommendation is therefore, that subsequent simulations within OceanNETs should use more realistic scenarios of CDR deployment.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".