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Record W2770483368 · doi:10.1016/j.funbio.2020.02.007

The Third International Symposium on Fungal Stress – ISFUS

2020· article· en· W2770483368 on OpenAlexaff
Alene Alder-Rangel, Alexander Idnurm, Alexandra Brand, Alistair J. P. Brown, Anna A. Gorbushina, Christina Kelliher, Cláudia Barbosa Ladeira de Campos, David E. Levin, Deborah Bell‐Pedersen, Ekaterina Dadachova, Florian F. Bauer, Geoffrey Michael Gadd, Gerhard H. Braus, Gilberto Úbida Leite Braga, Guilherme Thomaz Pereira Brancini, Graeme M. Walker, Irina S. Druzhinina, István Pócsi, Jan Dijksterhuis, Jesús Aguirre, John E. Hallsworth, Julia Schumacher, Koon Ho Wong, Laura Selbmann, Luis M. Corrochano, Martin Kupiec, Michelle Momany, Mikael Molin, Natalia Requena, Oded Yarden, Radamés J. B. Cordero, Rainer Fischer, Renata Castiglioni Pascon, Rocco L. Mancinelli, Tamás Emri, Thiago Olitta Basso, Drauzio E.N. Rangel

Bibliographic record

VenueFungal Biology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEntomopathogenic Microorganisms in Pest Control
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute of General Medical SciencesEuropean Regional Development FundDefense Threat Reduction AgencySocial Science Foundation of Jiangsu ProvinceNational Research FoundationNemzeti Kutatási Fejlesztési és Innovációs HivatalBundesministerium für Wirtschaft und EnergieConsejo Nacional de Ciencia y TecnologíaFundação de Amparo à Pesquisa do Estado de GoiásUniversidade de MacauVetenskapsrådetIsrael Science FoundationMinisterio de Ciencia, Innovación y UniversidadesNuclear Safety and Security CommissionDirectorate for Biological SciencesNational Institutes of HealthFundo para o Desenvolvimento das Ciências e da TecnologiaConselho Nacional de Desenvolvimento Científico e TecnológicoCarl Tryggers Stiftelse för Vetenskaplig ForskningCancerfondenDepartment of Agriculture, Environment and Rural Affairs, UK GovernmentAustrian Science FundMauritius Research CouncilDebreceni EgyetemIsrael Cancer Research FundDepartment for Environment and Heritage, Government of South AustraliaGovernment of Jiangsu ProvinceGrains Research and Development CorporationNatural Environment Research CouncilArts Council of Northern IrelandEuropean Social FundRoyal SocietyFundação de Amparo à Pesquisa do Estado de São PauloEuropean CommissionBiotechnology and Biological Sciences Research CouncilNational Aeronautics and Space AdministrationWellcome TrustCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAustralian Research CouncilBundesministerium für Wirtschaft und TechnologieDepartment for Employment and Learning, Northern IrelandDeutsche ForschungsgemeinschaftUniversidade Federal de GoiásSight Research UKUniversity of GeorgiaMedical Research CouncilMinistry of Science and Technology of the People's Republic of ChinaConsejo Nacional de Ciencia y Tecnología, Paraguay
KeywordsBiologyEcologyFungal growthAgricultureBotany

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0450.013

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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
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".

Quick stats

Citations20
Published2020
Admission routes1
Has abstractno

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