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Record W4281690511 · doi:10.1186/s13750-022-00265-z

Correction to: Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

2022· article· en· W4281690511 on OpenAlexaff
Eeva M. Soininen, Isabel C. Barrio, Ragnhild Bjørkås, Katrín Björnsdóttir, Dorothée Ehrich, Kelly A. Hopping, Elina Kaarlejärvi, Anders Lorentzen Kolstad, Svetlana Abdulmanova, Robert G. Björk, C. Guillermo Bueno, Isabell Eischeid, Rebecca Finger Higgens, Jennifer S. Forbey, Charles Gignac, Olivier Gilg, Michael den Herder, Hildur Søndergaard Holm, Bernice C. Hwang, Jane Uhd Jepsen, Stefaniya Kamenova, Ilona Kater, Amanda M. Koltz, J. A. Kristensen, Chelsea J. Little, Petr Macek, Karen Marie Mathisen, Daniel B. Metcalfe, Jesper Bruun Mosbacher, Martin Alfons Mörsdorf, Taejin Park, Jeffrey Propster, Aradhana Roberts, Emmanuel Serrano, Marcus P. Spiegel, Mariana Tamayo, Maria Tuomi, Megha Verma, Katariina Vuorinen, Maria Väisänen, René van der Wal, Megan Wilcots, Nigel G. Yoccoz, James D. M. Speed

Bibliographic record

VenueEnvironmental Evidence · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsSimon Fraser UniversityUniversité Laval
Fundersnot available
KeywordsVegetation (pathology)GeographyHerbivoreThe arcticArcticEcologyForestryEnvironmental sciencePhysical geographyEnvironmental resource managementOceanographyGeologyBiology

Abstract

fetched live from OpenAlex

Following publication of the original article [1], the authors reported that full author names need to appear in the article. Hence, we have update full author name in this correction. The original article [1] 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)0.010
metaresearch head score (Gemma)0.181
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.075
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.181
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0090.009
Science and technology studies0.0030.002
Scholarly communication0.0060.004
Open science0.0030.003
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0750.028

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 head0.040
GPT teacher head0.358
Teacher spread0.318 · 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 designSystematic review
Domainnot available
GenreEditorial

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
Published2022
Admission routes1
Has abstractyes

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