Bibliographic record
Abstract
Abstract. A modeling protocol is introduced (defined by a series of model simulations with specified model output). The protocol is designed to improve understanding of climate impacts from Marine Cloud Brightening (MCB) Climate Intervention. The model simulations are not intended to assess consequences from a realistic MCB deployment intended to achieve specific climate targets but instead to expose responses produced by MCB interventions in 6 regions with pervasive cloud systems that are often considered as candidate regions for such a deployment. A calibration step involving simulations with fixed sea surface temperatures is first used to identify a common forcing, and then coupled simulations with forcing in individual regions and combinations of regions are used to examine climate impacts. Synthetic estimates constructed by superposing responses from simulations with forcing in individual regions are considered as a means to approximate the climate impacts produced when MCB interventions are introduced in multiple regions. A few results comparing simulations from 3 modern climate models (CESM2, E3SMv2, UKESM1) are used to illustrate similarities and differences between model behavior and the utility of estimates of MCB climate responses that have been synthesized by summing responses introduced in individual regions. There are substantial differences in the cloud responses to aerosol injections between models, but the models often show strong similarities in precipitation and surface temperature response signatures when forcing is imposed with similar amplitudes in common regions.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.034 | 0.014 |
| Insufficient payload (model declined to judge) | 0.154 | 0.117 |
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".