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Record W3035720195 · doi:10.1111/ecog.05155

Transoceanic dispersal of terrestrial species by debris rafting

2020· article· en· W3035720195 on OpenAlexafffundabout
Zoë Lindo

Bibliographic record

VenueEcography · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiological dispersalEcologyGlacial periodDebrisBiogeographyBiodiversityFaunaLand bridgeShoreGeographyOceanographyGeologyBiologyPaleontology

Abstract

fetched live from OpenAlex

Rare, long‐distance dispersal events are a key process in generating and maintaining patterns in biological diversity and species distributions across space and time. The 9.0 magnitude earthquake that struck the eastern coast of Japan in 2011, and the subsequent 38 m high tsunami washed large amounts of shoreline debris into the Pacific Ocean that led to a large‐scale biological rafting event carrying nearly 300 marine species to the western shores of North America. Whether oceanic, trans‐Pacific dispersal via rafting generates long distance dispersal events for small, flightless, terrestrial species is unknown. By sampling beach debris associated with known hot‐spots of tsunami debris along the north and east shores of Graham Island, Haida Gwaii, Canada, I document significantly dissimilar invertebrate communities associated with tide‐line beach debris and the occurrence of several putative Japanese species of soil‐dwelling mites (Acari: Oribatida). Previous explanations of Haida Gwaii's unique flora and fauna have been attributed to a proximity to the Beringian land bridge and the accumulated evidence of near‐offshore glacial refugia during the last glacial period. However, my research also suggests that stochastic, trans‐Pacific rafting events contribute to the biodiversity and biogeography of soil communities on the west coast of North America.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.013
GPT teacher head0.182
Teacher spread0.169 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations21
Published2020
Admission routes3
Has abstractyes

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