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Record W6939058976 · doi:10.6068/dp1752dbf3e6921

TREND: Organisation for Economic Co-operation and Development (OECD). OECD Factbook 2014: Economic, Environmental and Social Statistics: Energy and Transportation - Energy Supply | Country: Russia | Socioeconomic Indicator: Total Primary Energy Supply, 1990 - 2011. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-001-013

2020· other· en· W6939058976 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2020
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary energyEnergy supplyEnergy subsidiesEnergy policyResource (disambiguation)Unit (ring theory)Energy sourceEnergy consumptionSocioeconomic development

Abstract

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Organisation for Economic Co-operation and Development (OECD). OECD Factbook 2014: Economic, Environmental and Social Statistics: Energy and Transportation - Energy Supply | Country: Russia | Socioeconomic Indicator: Total Primary Energy Supply, 1990 - 2011. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-001-013 Dataset: Refers to total primary energy supply (TPES), which equals production plus imports minus exports minus international bunkers plus or minus stock changes, in terms of million tons of oil equivalent (Mtoe) . This dataset provides indicators included in the OECD Factbook 2014: Economic, Environmental, and Social Statistics, updated annually by the Organisation for Economic Co-operation and Development (OECD). Indicators, reported in 12 broad subject areas, cover a wide range of topics: agriculture, economic production, education, energy, environment, foreign aid, health, industry, information and communications, international trade, labor force, population, taxation, public expenditure, and research and development. Data are provided for all OECD member countries and Brazil, China, India, Indonesia, Russia, and South Africa, where available. NOTE: The data presented here are copyrighted by OECD and reproduction is subject to OECD permissions policies: See http://www.oecd.org/rights for further information. Indicator descriptions are based on the OECD Factbook 2014. http://stats.oecd.org/BrandedView.aspx?oecd_bv_id=factbook-data-en&doi=data-00590-en The International Energy Agency (IEA) energy balance methodology is based on the calorific content of the energy commodities and a common unit of account. The unit of account adopted is the ton of oil equivalent (toe), which is defined as 107 kilocalories (41.868 gigajoules). The IEA balances are calculated using the physical energy content method to calculate the primary energy equivalent. While every effort is made to ensure the accuracy of the data, quality is not homogeneous for all countries and regions. In some countries, data are based on secondary sources, and where incomplete or unavailable, the IEA has made estimates. In general, data are likely to be more accurate for production and trade than for international bunkers or stock changes. Moreover, statistics for biofuels and waste are less accurate than those for traditional commercial energy data in most countries. Category: International Relations and Trade, Energy Resources and Industries Subject: Imports, Energy Supply, Exports, Energy Production Source: Organisation for Economic Co-operation and Development (OECD) Established in 1961, when 18 European countries plus the United States and Canada joined together to create an organization dedicated to global development, the Organisation for Economic Co-operation and Development (OECD) today includes 34 member countries from around the globe, ranging from North and South America to Europe and the Asia-Pacific region. Member countries include many of the world’s advanced countries as well as emerging nations. The OECD mission remains the promotion of policies that will improve the economic and social well-being of people around the world. The OECD collects and analyzes data on a broad range of topics to help governments foster prosperity and fight poverty through economic growth and financial stability, at the same time taking the environmental implications of economic and social development into account. The OECD Secretariat collects and analyzes data, after which committees discuss policy regarding this information, the Council makes decisions, and then governments implement recommendations. The performance of individual countries is monitored following implementation via a system of multilateral surveillance and a peer review process. The OECD is headquartered in Paris, France, and it is funded by its member countries. National contributions are based on a formula that takes account of the size of each member's economy. The largest contributor is the United States, which provides nearly 24% of the budget, followed by Japan. http://www.oecd.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)0.002
metaresearch head score (Gemma)0.018
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 score0.095
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.032
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0950.120

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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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