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Record W2912244478 · doi:10.1098/rstb.2018.0186

Understanding interactions between plasticity, adaptation and range shifts in response to marine environmental change

2019· review· en· W2912244478 on OpenAlexaff
Jennifer M. Donelson, Jennifer M. Sunday, Will F. Figueira, Juan Diego Gaitán‐Espitía, Alistair J. Hobday, Craig R. Johnson, Jeffrey M. Leis, SD Ling, Dustin J. Marshall, John M. Pandolfi, GT Pecl, Giverny G. Rodgers, David J. Booth, Philip L. Munday

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsEnvironmental changeAdaptation (eye)Climate changePhenotypic plasticityEcologyRange (aeronautics)EcosystemLocal adaptationBiologyPopulationNeuroscience

Abstract

fetched live from OpenAlex

Climate change is leading to shifts in species geographical distributions, but populations are also probably adapting to environmental change at different rates across their range. Owing to a lack of natural and empirical data on the influence of phenotypic adaptation on range shifts of marine species, we provide a general conceptual model for understanding population responses to climate change that incorporates plasticity and adaptation to environmental change in marine ecosystems. We use this conceptual model to help inform where within the geographical range each mechanism will probably operate most strongly and explore the supporting evidence in species. We then expand the discussion from a single-species perspective to community-level responses and use the conceptual model to visualize and guide research into the important yet poorly understood processes of plasticity and adaptation. This article is part of the theme issue 'The role of plasticity in phenotypic adaptation to rapid environmental change'.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.348
GPT teacher head0.327
Teacher spread0.021 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations250
Published2019
Admission routes1
Has abstractyes

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