Carbon Dioxide Emission Pathways Avoiding Dangerous Ocean Impacts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Anthropogenic emissions of greenhouse gases could lead to undesirable effects on oceans in coming centuries. Drawing on recommendations published by the German Advisory Council on Global Change, levels of unacceptable global marine change (so-called guardrails) are defined in terms of global mean temperature, sea level rise, and ocean acidification. A global-mean climate model [the Aggregated Carbon Cycle, Atmospheric Chemistry and Climate Model (ACC2)] is coupled with an economic module [taken from the Dynamic Integrated Climate–Economy Model (DICE)] to conduct a cost-effectiveness analysis to derive CO2 emission pathways that both minimize abatement costs and are compatible with these guardrails. Additionally, the “tolerable windows approach” is used to calculate a range of CO2 emissions paths that obey the guardrails as well as a restriction on mitigation rate. Prospects of meeting the global mean temperature change guardrail (2° and 0.2°C decade−1 relative to preindustrial) depend strongly on assumed values for climate sensitivity: at climate sensitivities >3°C the guardrail cannot be attained under any CO2 emissions reduction strategy without mitigation of non-CO2 greenhouse gases. The ocean acidification guardrail (0.2 unit pH decline relative to preindustrial) is less restrictive than the absolute temperature guardrail at climate sensitivities >2.5°C but becomes more constraining at lower climate sensitivities. The sea level rise and rate of rise guardrails (1 m and 5 cm decade−1) are substantially less stringent for ice sheet sensitivities derived in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report, but they may already be committed to violation if ice sheet sensitivities consistent with semiempirical sea level rise projections are assumed.
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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 | 0.001 | 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 it