Accelerating Clean Energy Transitions to Safeguard Human Health and Survival
Bibliographic record
Abstract
The year 2023 was the warmest year in the 174-year global instrumental record. The year was also marked by a series of climate-related extreme events, including heat waves, storms, and wildfires that caused widespread economic and health impacts. The 28th Conference of the Parties of the United Nations Framework Convention on Climate Change called for transitioning away from fossil fuels and accelerating action in this critical decade. All countries must move rapidly toward net zero emissions and scale up their action to ensure achievement of the Paris climate goals-viz., limiting the global temperature increase from preindustrial levels to well below 2 °C and pursuing efforts to keep it below 1.5 °C. There is growing concern about whether the goal of limiting global warming to 1.5 °C is still achievable. We believe that it is still possible to limit warming to 1.5 °C if we take seven essential actions so human health and survival can be safeguarded: scaling up the energy transition to achieve carbon neutrality before the middle of this century; rapidly phasing out the construction of new fossil fuel exploration and infrastructure; enforcing an international carbon price; tightening emission targets across both the global north and south; promoting and adopting low-consumption lifestyle as the social norm; engaging in transformative change to simultaneously act on climate, biodiversity, equity, human health, and well-being; and boosting collective efforts and strengthening international cooperation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".