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Record W4393863158 · doi:10.1126/sciadv.adm6755

The ratting of North America: A 350-year retrospective on <i>Rattus</i> species compositions and competition

2024· article· en· W4393863158 on OpenAlexaff
Eric Guiry, Ryan Kennedy, David Orton, Philip Armitage, John R. Bratten, Charles Dagneau, Shannon Lee Dawdy, Susan D. deFrance, Barry C. Gaulton, David Givens, Olivia Hall, A Laberge, Michael Lavin, Henry M. Miller, Mary F. Minkoff, Tatiana Niculescu, Stéphane Noël, Barnet Pavão-Zuckerman, Leah Stricker, Matt Teeter, Martin Welker, Jennifer Wilkoski, Paul Szpak, Michael Buckley

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversité LavalUniversité du QuébecMemorial University of NewfoundlandParks CanadaTrent University
Fundersnot available
KeywordsCompetition (biology)GeographyZoologyBiologyEcology

Abstract

fetched live from OpenAlex

) rats on human society are well documented-including the spread of disease, broad-scale environmental destruction, and billions spent annually on animal control-little is known about their ecology and behavior in urban areas due to the challenges of studying animals in city environments. We use isotopic and ZooMS analysis of archaeological (1550s-1900 CE) rat remains from eastern North America to provide a large-scale framework for species arrival, interspecific competition, and dietary ecology. Brown rats arrived earlier than expected and rapidly outcompeted black rats in coastal urban areas. This replacement happened despite evidence that the two species occupy different trophic positions. Findings include the earliest molecularly confirmed brown rat in the Americas and show a deep ecological structure to how rats exploit human-structured areas, with implications for understanding urban zoonosis, rat management, and ecosystem planning as well as broader themes of rat dispersal, phylogeny, evolutionary ecology, and climate 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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
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 studies0.0010.004
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 head0.007
GPT teacher head0.253
Teacher spread0.245 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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