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Record W2991900227 · doi:10.1007/s13280-019-01296-6

Non-native vascular flora of the Arctic: Taxonomic richness, distribution and pathways

2019· article· en· W2991900227 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

VenueAMBIO · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsYukon Department of EnvironmentCanadian Museum of Nature
FundersInternational Arctic Research Center, University of Alaska, FairbanksDirectorate for Biological SciencesUniversity of Alaska AnchorageWashington State University
KeywordsArcticVascular plantTaxonFlora (microbiology)Arctic vegetationEcologyNative plantSpecies richnessBiologyThe arcticIntroduced speciesInvasive speciesGeographyOceanographyTundra

Abstract

fetched live from OpenAlex

We present a comprehensive list of non-native vascular plants known from the Arctic, explore their geographic distribution, analyze the extent of naturalization and invasion among 23 subregions of the Arctic, and examine pathways of introductions. The presence of 341 non-native taxa in the Arctic was confirmed, of which 188 are naturalized in at least one of the 23 regions. A small number of taxa (11) are considered invasive; these plants are known from just three regions. In several Arctic regions there are no naturalized non-native taxa recorded and the majority of Arctic regions have a low number of naturalized taxa. Analyses of the non-native vascular plant flora identified two main biogeographic clusters within the Arctic: American and Asiatic. Among all pathways, seed contamination and transport by vehicles have contributed the most to non-native plant introduction in the Arctic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.169
Teacher spread0.160 · 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