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Record W3093290892 · doi:10.1016/j.envsoft.2020.104900

Speaking their language – Development of a multilingual decision-support tool for communicating invasive species risks to decision makers and stakeholders

2020· article· en· W3093290892 on OpenAlexaff
Gordon H. Copp, Lorenzo Vilizzi, Hui Wei, Shan Li, Marina Piria, Abbas J. Al-Faisal, David Almeida, Usman Atique, Zainab Al-Wazzan, Rigers Bakiu, Tea Bašić, Thuyet D. Bui, João Canning‐Clode, Nuno Castro, Ratcha Chaichana, Tülin Çoker, Dimitriy Dashinov, Fitnat Güler Ekmekçı, Tibor Erős, Árpád Ferincz, Daniela Giannetto, Allan S. Gilles, Łukasz Głowacki, Philippe Goulletquer, Е. А. Интересова, Sonia Iqbal, Katarína Jakubčinová, Kamalaporn Kanongdate, Jeong-Eun Kim, Oldřich Kopecký, Vasil Kostov, Nicholas Koutsikos, Sebastian Kozic, Petra Kristan, Yoshihisa Kurita, Hwang-Goo Lee, R.S.E.W. Leuven, Tatsiana Lipinskaya, Juliane Lukas, Agnese Marchini, Ana Isabel González Martínez, Laurence Masson, Daniyar Memedemin, Seyed Daryoush Moghaddas, João Gama Monteiro, Levan Mumladze, Rahmat Naddafi, NĂVODARU Ion, Karin H. Olsson, Norio Onikura, Daniele Paganelli, Richard Thomas B. Pavia, Costas Perdikaris, Renanel Pickholtz, Dariusz Pietraszewski, Meta Povž, Cristina Preda, Milica Ristovska, Karin Rosíková, José Maria Santos, Vitaliy Semenchenko, Wansuk Senanan, Predrag Simonović, Evangelia Smeti, Barbora Števove, Kristína Slovák Švolíková, Kieu Anh T. Ta, Ali Serhan Tarkan, Nildeniz Top, Elena Tricarico, E. Uzunova, Leonidas Vardakas, Hugo Verreycken, Grzegorz Zięba, Roberto Mendoza

Bibliographic record

VenueEnvironmental Modelling & Software · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of Northern British Columbia
FundersFundação para a Ciência e a TecnologiaInstituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção
KeywordsDecision support systemFirst languageComputer scienceKnowledge managementBusinessArtificial intelligenceLinguistics

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0220.008

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 head0.096
GPT teacher head0.268
Teacher spread0.172 · 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 designSimulation or modeling
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

Citations75
Published2020
Admission routes1
Has abstractno

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