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Enregistrement W4242340565 · doi:10.1088/1755-1315/432/1/011001

Preface

2020· article· en· W4242340565 sur OpenAlexaboutno aff

Notice bibliographique

RevueIOP Conference Series Earth and Environmental Science · 2020
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueGeological Modeling and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChinaBeijingLibrary scienceSustainable developmentPolitical scienceChinese academy of sciencesEngineeringComputer science

Résumé

récupéré en direct d'OpenAlex

It is our great pleasure to welcome you to 2019 International Conference on Resources and Environmental Research (ICRER 2019) which was successfully held in Shandong University, Qingdao, China during October 25-27, 2019. ICRER 2019 is co-organized by Shandong University, assisted by Xiamen University of Technology, South-South Collaborative and Sustainable Development Center, International Society for Environmental Information Sciences (ISEIS) and Shandong University of Technology, Hong Kong Chemical, Biological & Environmental Engineering Society (HKCBEES), Environment and Agriculture Society (EAS). ICRER 2019 is dedicated to issue related to resources and environmental research. The major goal and feature of the conference is to bring academic scientists, engineers, industry researchers together to exchange and share their experiences and research results, and discuss the practical challenges encountered and the solutions adopted. Prof. Shuguang Wang from School of Environment Science and Engineering, Shandong University, China has been invited to do the welcome address; Prof. Edward McBean from University of Guelph, Canada has shared his keynote speech” Challenges for the Future, for Sponge City”; Prof. Yongping Li from Beijing Normal University, China has presented her keynote speech “Sustainable water resources management under uncertainty – A case study of Central Asia”; Prof.Zhijun Peng from University of Bedfordshire, UK has given his keynote speech” The Way to Save CO 2 Emissions with BEVx (Plug-in Pure Battery Electric Vehicles)”; Prof. Christophe Guimbaud from Université Orléans, France has presented his keynote speech” Impact of global changes on Greenhouse Gas (GHG) exchanges with atmosphere for sphagnum type peatlands: field study and modelling approach for methane emissions”; Dr. Pengfei Xia from Center for Applied Geosciences, University of Tübingen, Tübingen, Germany has given his keynote speech” Harnessing synthetic biology for recycling carbon dioxide”; Prof. Gordon Huang from University of Regina, Canada has shared his keynote speech “The Way to Save CO 2 Emissions with BEVx (Plug-in Pure Battery Electric Vehicles). Many researchers, engineers, academicians as well as industrial professionals from all over the world have presented their research results and development activities. There were three topics for all the session presentations: Modeling of energy management systems, Energy and environmental Studies, Technologies of power and energy engineering. It will be a golden opportunity for the students, researchers and engineers to interact with the experts and specialists to get their advice or consultation on technical matters, sales and marketing strategies. This conference proceeding presents a selection from papers submitted to the conference from universities, research institutes and industries. All of the papers were subjected to peer-review by conference committee members and international reviewers. The papers selected depended on their quality and their relevancy to the conference. The volume tends to present to the readers the recent advances in the field of resources and environmental research and various related areas. We would like to thank all the authors who have contributed to this volume and also to the organizing committees, reviewers, speakers, chairpersons, sponsors and all the conference participants for their support to ICRER 2019. Prof. Shuguang Wang School of Environment Science and Engineering, Shandong University, China November 25,2019

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,539
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,182
Écart entre enseignants0,161 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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