MétaCan
Menu
Back to cohort
Record W2980151843 · doi:10.1007/s13280-019-01262-2

Correction to: Documenting lemming population change in the Arctic: Can we detect trends?

2019· erratum· en· W2980151843 on OpenAlexaff
Dorothée Ehrich, Niels Martin Schmidt, Gilles Gauthier, Ray T. Alisauskas, Anders Angerbjörn, Karin Clark, Frauke Ecke, Nina E. Eide, Erik Framstad, Jay Frandsen, Alastair Franke, Olivier Gilg, Marie‐Andrée Giroux, Heikki Henttonen, Birger Hörnfeldt, Rolf A. Ims, G. D. Kataev, С. П. Харитонов, Siw T. Killengreen, Charles J. Krebs, Richard B. Lanctot, Nicolas Lecomte, Irina E. Menyushina, Douglas W. Morris, Guy Morrisson, Lauri Oksanen, Tarja Oksanen, Johan Olofsson, Ivan Pokrovsky, Igor Popov, Donald G. Reid, James D. Roth, Sarah T. Saalfeld, Gustaf Samelius, Benoît Sittler, С. М. Слепцов, Paul A. Smith, Aleksandr Sokolov, Natalia Sokolova, Mikhail Soloviev, Diana Solovyeva

Bibliographic record

VenueAMBIO · 2019
Typeerratum
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of ManitobaWildlife Conservation Society CanadaLakehead UniversityUniversity of British ColumbiaUniversité de MonctonUniversité LavalUniversity of AlbertaParks CanadaCarleton UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsComputer scienceNatural language processingPopulationArtificial intelligenceInformation retrievalData scienceHistorySociologyDemography

Abstract

fetched live from OpenAlex

In the original published article, some of the symbols in figure 1A were modified incorrectly during the typesetting and publication process. The correct version of the figure is provided in this correction.

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)0.003
metaresearch head score (Gemma)0.050
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 score0.077
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.050
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0770.045

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 head0.032
GPT teacher head0.278
Teacher spread0.247 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAMBIOSame topicSpecies Distribution and Climate ChangeFrench-language works237,207