MétaCan
Menu
Back to cohort
Record W3205408017 · doi:10.1126/science.abf2343

Physical disturbance by recovering sea otter populations increases eelgrass genetic diversity

2021· article· en· W3205408017 on OpenAlexaff
Erin U. Foster, Jane C. Watson, Matthew A. Lemay, M. Tim Tinker, James A. Estes, Rebecca S. Piercey, Lauren H. Henson, Carol Ritland, Allyson E. Miscampbell, Linda M. Nichol, Margot Hessing‐Lewis, Anne K. Salomon, Chris T. Darimont

Bibliographic record

VenueScience · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsSimon Fraser UniversityUniversity of British ColumbiaRaincoast Conservation FoundationVancouver Island UniversityFisheries and Oceans CanadaTula FoundationUniversity of Victoria
Fundersnot available
KeywordsZostera marinaEcologyOtterDisturbance (geology)Genetic diversitySpecies richnessBiologyPredationEcosystemPopulationSeagrass

Abstract

fetched live from OpenAlex

) meadows increases genetic diversity by promoting conditions for sexual reproduction of plants. Eelgrass allelic richness and genotypic diversity were, respectively, 30 and 6% higher in areas where recovering sea otter populations had been established for 20 to 30 years than in areas where they had been present <10 years or absent >100 years. The influence of sea otter occupancy on the aforementioned measures of genetic diversity was stronger than those of depth, temperature, latitude, or meadow size. Our findings reveal an underappreciated evolutionary process by which megafauna may promote genetic diversity and ecological resilience.

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)0.000
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.210
Teacher spread0.195 · 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

Citations30
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueScienceSame topicMarine and coastal plant biologyFrench-language works237,207