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Record W7002248158

The Moral Imperative of Green Nuclear Energy Production

2020· article· en· W7002248158 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal warmingClimate changeGreenhouse gasLimitingRunaway climate changeGreenhouse effectGlobal temperatureArcticThe arctic
DOInot available

Abstract

fetched live from OpenAlex

A climate crisis is upon us. Human-caused global warming is already changing our planet's climate in dramatic ways, and the effects are forecast to become far worse by the end of the century without rapid and radical changes to the global energy economy and the other forms of human activity that generate CO2 and other greenhouse gases, such as methane. The ten hottest years on record have all occurred since 1998, with the past five years topping the list. We already see the disappearance of the arctic ice pack, massive glacial melting in Greenland, sea-level rise, massive wildfires in northern Canada, the Amazonian rainforest, the western United States, Spain, France, and Siberia, ever more violent storms, and rapidly warming ocean temperatures. If we are to avoid potentially catastrophic climate change by 2100 by limiting the rise of global mean surface air temperature to 2.OC, compared to the twentieth-century average, then we must aim for the complete decarbonization of the energy economy, which will then also include a more or less totally electrified, global transportation system, by no later than 2065. The question is, "How do we do that?"

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.031
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.025
GPT teacher head0.272
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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