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Record W4413054501 · doi:10.1038/s41597-025-05750-x

Correction: Global Impacts Dataset of Invasive Alien Species (GIDIAS)

2025· erratum· en· W4413054501 on OpenAlexaff
Sven Bacher, Ellen Ryan‐Colton, Mario Coiro, Phillip Cassey, Martín A. Núñez, Michael Ansong, Katharina Dehnen‐Schmutz, George Fayvush, Romina Fernández, Ankila J. Hiremath, Makihiko Ikegami, Angeliki F. Martinou, Shana M. McDermott, Cristina Preda, Montserrat Vilà, Olaf L. F. Weyl, Ιoanna Angelidou, Katerina Athanasiou, Vidyadhar Atkore, Jacob N. Barney, Tim M. Blackburn, Eckehard G. Brockerhoff, Clinton Carbutt, Luca Carisio, Pilar Castro‐Díez, Vanessa Céspedes, Aikaterini Christopoulou, Diego F. Cisneros‐Heredia, Meghan Cooling, Maarten de Groot, Jakovos Demetriou, James W. E. Dickey, Virginia G. Duboscq-Carra, Regan Early, Thomas Evans, Paola T Flores-Males, Belinda Gallardo, Monica A. M. Gruber, Cang Hui, Jonathan M. Jeschke, Natalia Zoe Joelson, Mohd Asgar Khan, Sabrina Kumschick, Lori Lach, Katharina Lapin, Simone Lioy, Chunlong Liu, Zoe J MacMullen, Manuela A Mazzitelli, John Measey, Agata A Mrugała-Koese, Camille Musseau, Helen F. Nahrung, Alessia Lucia Pepori, Luis R. Pertierra, Elizabeth F. Pienaar, Petr Pyšek, Gonzalo Rivas‐Torres, Julissa Rojas‐Sandoval, Ned L. Ryan‐Schofield, Rocío Sánchez, Alberto Santini, Davide Santoro, Riccardo Scalerà, Lisanna Schmidt, Tinyiko C. Shivambu, Sima Sohrabi, Elena Tricarico, Alejandro Trillo, Pieter van ’t Hof, Lara Volery, Tsungai A. Zengeya

Bibliographic record

VenueScientific Data · 2025
Typeerratum
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsAlienEcologyAlien speciesInvasive speciesGeographyData scienceBiologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

In this article the author’s name Thomas G. Evans was incorrectly written as Thomas E. Evans. The original article has been corrected.

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 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)0.073
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: Dataset · Consensus signal: none
Teacher disagreement score0.176
Threshold uncertainty score0.588

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.073
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.008
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0030.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.1760.094

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 head0.059
GPT teacher head0.302
Teacher spread0.244 · 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
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractno

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