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Full Conference Title: The 10th International Conference on Petroleum Geochemistry and Exploration in the Afro-Asian Region (AAAPG 2019)

2019· article· en· W4245982326 on OpenAlexaboutno aff

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsChinaPetroleumGeochemistryIsotope geochemistryChinese academy of sciencesPetroleum geochemistryLibrary scienceGeologyEarth scienceStructural basinRegional sciencePolitical scienceGeographySource rockArchaeology

Abstract

fetched live from OpenAlex

Date of the Event: 10-12 May, 2019 Location: Guangzhou, Guangdong, China Preface The 10th International Conference on Petroleum Geochemistry and Exploration in the Afro-Asian Region (AAAPG2019) is an international forum where the latest research and technological advances related to petroleum geochemistry in the Afro-Asian region are presented and exchanged. The objective of the AAAPG2019 is to promote international and interdisciplinary exchange of information, to foster co-operation among researchers in academia, research institutes and industry, and to stimulate growth and advances in the field of petroleum geochemistry across the Afro-Asian region. Scientific topics discussed in this conference include: • 1. Fossil fuel resources of Afro-Asian region & exploration priorities. • 2. Geochemistry of source rocks, petroleum accumulation and alteration. • 3. Oil and gas generation and migration. • 4. Biomarker and isotopic geochemistry. • 5. Novel geochemical technologies and basin modelling. • 6. Geochemistry and petro-physics of unconventional oil & gas. • 7. Environmental geochemistry and bio-geochemistry. • 8. International perspectives and alternative energy sources. The AAAPG2019 was held in Guangzhou, China, on May 10-12, 2019, 220 representatives from 18 countries and regions including India, Indonesia, Saudi Arabia, Nigeria, Sudan, the United States, the United Kingdom, Germany, Canada, Australia, Poland, and China participated in the meeting. A total of 141 abstracts were received, 74 abstracts were presented in oral and 41 abstracts were presented in poster. The papers included in this volume are expanded papers from parts of the abstracts presented in this conference. This volume was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA14010103), China national major S&T program (2017ZX05008-002-030). Guangzhou, China 30 July, 2019 Yunpeng Wang

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.472
Threshold uncertainty score0.753

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.4720.332

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 head0.013
GPT teacher head0.198
Teacher spread0.186 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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