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Record W2141928869 · doi:10.1002/ece3.1439

A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels

2015· article· en· W2141928869 on OpenAlexaff
Jani Heino, Adriano S. Melo, Luís Mauricio Bini, Florian Altermatt, Salman Abdo Al‐Shami, David G. Angeler, Núria Bonada‬‬‬‬‬‬‬‬‬‬‬, Cecilia Brand, Marcos Callisto, Karl Cottenie, Olivier Dangles, David Dudgeon, Andrea C. Encalada, Emma Göthe, Mira Grönroos, Neusa Hamada, Dean Jacobsen, Victor Lemes Landeiro, Raphael Ligeiro, Renato Tavares Martins, María Laura Miserendino, Che Salmah Md Rawi, Marciel Élio Rodrigues, Fábio de Oliveira Roque, Leonard Sandin, Dénes Schmera, Luciano Fabris Sgarbi, John P. Simaika, Tadeu Siqueira, Ross M. Thompson, Colin R. Townsend

Bibliographic record

VenueEcology and Evolution · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsUniversity of Guelph
FundersAgência Nacional de Energia ElétricaMinisterio del Ambiente, Agua y Transición EcológicaMinisterio de Ciencia y TecnologíaInstituto Nacional de Pesquisas da AmazôniaFundação de Amparo à Pesquisa do Estado do AmazonasFundação de Amparo à Pesquisa do Estado de Minas GeraisCompanhia Energética de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoMinistry of Higher Education, MalaysiaConsejo Nacional de Investigaciones Científicas y TécnicasAcademy of FinlandMinistry of EnvironmentCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMetacommunityBeta diversityEcologyBiodiversityLatitudeSpatial variabilityAlpha diversityRange (aeronautics)Aquatic insectAssemblage (archaeology)Distance decaySpatial ecologyGeographyEnvironmental scienceBiologyBiological dispersalHabitatPopulationStatistics

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.117
GPT teacher head0.257
Teacher spread0.140 · 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 designObservational
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".

Quick stats

Citations198
Published2015
Admission routes1
Has abstractyes

Explore more

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