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Record W6938914214 · doi:10.6068/dp14d6f67f7467

TREND: World Bank. Climate Change Data: GHG Emissions and Energy Use | Country: Afghanistan, Australia, Barbados, Brazil, Canada, Central African Republic, Costa Rica, Cuba, Egypt, Iran, Iraq, Israel, Nigeria, Russia, United Kingdom, United States | Socioeconomic Indicator: CO2 emissions per capita, 1998 - 2008. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 051-005-004

2015· other· en· W6938914214 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasClimate changeGlobal warmingPopulationEnergy intensityClimate change mitigationEfficient energy useEnvironmental data

Abstract

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World Bank. Climate Change Data: GHG Emissions and Energy Use | Country: Afghanistan, Australia, Barbados, Brazil, Canada, Central African Republic, Costa Rica, Cuba, Egypt, Iran, Iraq, Israel, Nigeria, Russia, United Kingdom, United States | Socioeconomic Indicator: CO2 emissions per capita, 1998 - 2008. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 051-005-004 Dataset: Presents data on the emissions of greenhouse gases (GHG) and energy use, including measures of energy efficiency and carbon intensity of economy. The data are sourced by the World Bank from national and international organizations, including the Carbon Dioxide Information Analysis Center, Environmental Sciences Division, Oak Ridge National Laboratory (Tennessee, United States), and the International Energy Agency. The World Bank Climate Change Data present statistics that relate to climate change, development, and the relationship between the two. The over 70 indicators included in the dataset are grouped into four areas: Size of the Economy, which presents data on population size and Gross Domestic Product (GDP); Exposure to Impacts, which provides data that suggest the degree to which a country or region may be exposed to changes in the prevailing climate and natural environment, with potential impacts on human health, food security, water supply, and physical infrastructure; Resilience, which includes data on the capacity of human systems to cope with the impacts of climate change, such as economic capacity, institutional capacity, and extent of public infrastructure; GHG Emissions and Energy Use, which includes data on the emissions of greenhouse gases (GHG) and energy use, including measures of energy efficiency and carbon intensity of economy. Data are shown for economies with populations greater than 30,000 or for smaller economies if they are members of the World Bank. Where available data are provided for all countries and by nation covering the period 1990-2011. http://data.worldbank.org/topic/climate-change Category: Natural Resources and Environment, Energy Resources and Industries Subject: Air Pollution, Energy Demand, Climate Change, Energy Consumption, Air Pollutants Source: World Bank Founded in 1944 as a result of the Bretton Woods Conference, the World Bank is an independent specialized agency of the United Nations that provides financial and technical assistance to developing countries in order to foster economic growth and reduce poverty. Today, the World Bank Group comprises five institutions owned by 187 member countries: the International Bank for Reconstruction and Development (IBRD), which focuses on middle-income and creditworthy poor countries; the International Development Association (IDA), which focuses on least developed countries; the International Finance Corporation (IFC); the Multilateral Guarantee Agency (MIGA); and the International Centre for the Settlement of Investment Disputes (ICSID). http://www.worldbank.org/

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.906
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.016
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0600.070

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 head0.048
GPT teacher head0.289
Teacher spread0.241 · 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 designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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