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Record W3013162427 · doi:10.1038/s41598-020-62340-0

First mussel settlement observed in Antarctica reveals the potential for future invasions

2020· article· en· W3013162427 on OpenAlexaff
Leyla Cárdenas, Jean‐Charles Leclerc, Paulina Bruning, Ignacio Garrido, Camille Détrée, Álvaro Figueroa, Marcela Astorga, Jorge M. Navarro, Ladd E. Johnson, James T. Carlton, Luis Miguel Pardo

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversité Laval
FundersFondo de Financiamiento de Centros de Investigación en Áreas PrioritariasFondo Nacional de Desarrollo Científico y Tecnológico
KeywordsShetlandBiodiversityBenthosPeninsulaEcologyMarine ecosystemZebra musselEcosystemAnthropoceneClimate changeOceanographyHabitatGeographyMarine lifeFisheryBiologyMusselBenthic zoneGeology

Abstract

fetched live from OpenAlex

Global biodiversity is both declining and being redistributed in response to multiple drivers characterizing the Anthropocene, including synergies between biological invasions and climate change. The Antarctic marine benthos may constitute the last biogeographic realm where barriers (oceanographic currents, climatic gradients) have not yet been broken. Here we report the successful settlement of a cohort of Mytilus cf. platensis in a shallow subtidal habitat of the South Shetland Islands in 2019, which demonstrates the ability of this species to complete its early life stages in this extreme environment. Genetic analyses and shipping records show that this observation is consistent with the dominant vectors and pathways linking southern Patagonia with the Antarctic Peninsula and demonstrates the potential for impending invasions of Antarctic ecosystems.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.252
Teacher spread0.198 · 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 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

Citations71
Published2020
Admission routes1
Has abstractyes

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