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Record W2919277492 · doi:10.1002/fee.2020

Alien versus native species as drivers of recent extinctions

2019· review· en· W2919277492 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsIUCN Red ListExtinction (optical mineralogy)AlienBiodiversityThreatened speciesIntroduced speciesEcologyBiologyInvasive speciesEcosystemAlien speciesVulnerable speciesEndangered speciesGeographyHabitatPopulation

Abstract

fetched live from OpenAlex

Native plants and animals can rapidly become superabundant and dominate ecosystems, leading to claims that native species are no less likely than alien species to cause environmental damage, including biodiversity loss. We compared how frequently alien and native species have been implicated as drivers of recent extinctions in a comprehensive global database, the 2017 International Union for Conservation of Nature ( IUCN ) Red List of Threatened Species. Alien species were considered to be a contributing cause of 25% of plant extinctions and 33% of animal extinctions, whereas native species were implicated in less than 5% and 3% of plant and animal extinctions, respectively. When listed as a putative driver of recent extinctions, native species were more often associated with other extinction drivers than were alien species. Our results offer additional evidence that the biogeographic origin, and hence evolutionary history, of a species are determining factors of its potential to cause disruptive environmental impacts.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.260
Teacher spread0.237 · 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