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Record W4248570432 · doi:10.5539/jps.v6n1p103

Reviewer Acknowledgements for Journal of Plant Studies, Vol. 6, No. 1

2017· article· en· W4248570432 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Plant Studies · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary scienceChristian ministryPolitical scienceResearch centerLaw

Abstract

fetched live from OpenAlex

Journal of Plant Studies wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal are greatly appreciated.Journal of Plant Studies is recruiting reviewers for the journal. If you are interested in becoming a reviewer, we welcome you to join us. Please find the application form and details at http://www.ccsenet.org/reviewer and e-mail the completed application form to jps@ccsenet.org.Reviewers for Volume 6, Number 1Adriana F. Sestras, University of Agricultural Sciences and Veterinary Medicine, RomaniaAna Simonovic, Institute for Biological Research "Sinisa Stankovic", SerbiaArthur T. O. Melo, College of Life Sciences and Agriculture, University of New Hampshire, United StatesBing Wang, Iowa State University, United StatesChrystian Iezid Maia e Almeida Feres, Tocantins Federal University, BrazilHoma Mahmoodzadeh, Department of Biology, Mashhad Branch, Islamic Azad University, Mashhad, IranKinga Kostrakiewicz-Gieralt, Institute of Botany, Jagiellonian University, PolandKirandeep Kaur Mani, California seed and Plant Labs, Pleasant Grove, CA, United States of AmericaMalgorzata Pietrowska-Borek, Poznan University of Life Sciences, PolandMartina Pollastrini, University of Florence, ItalyMassimo Zacchini, National Research Council of Italy (CNR), ItalyMelekber Sulusoglu, Arslanbey Vocational School Kocaeli University, TurkeyMontaser Fawzy Abdel-Monaim, Plant Pathology Res. Instatute, Agric. Res. Center, EgyptPanagiotis Madesis, Institiute of Applied Biosciences (CE.R.T.H.)/INAB, GreecePeter R. Greene, BGKT Consulting Ltd. Huntington, New York 11743, United StatesRosana Noemi Malpassi, Universidad Nacional de Rio Cuarto, ArgentinaSaid Laarabi, University Mohammed V/Ministry of National Education, MoroccoSlawomir Borek, Adam Mickiewicz University, PolandSuheb Mohammed, University of Virginia, United StatesVikas Mishra, Paher University, IndiaXiaomin Wu, Loyola University Chicago, United StatesYa-Yi Huang, Institution of Plant and Microbial Biology, Academia Sinica, Taiwan

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.421
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.128
GPT teacher head0.336
Teacher spread0.208 · 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 teacher head, not a consensus.

Study designNot applicable
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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