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Record W2271795322 · doi:10.1038/srep20194

Individual and population-level responses to ocean acidification

2016· article· en· W2271795322 on OpenAlexaff
Ben P. Harvey, Niall J. McKeown, Samuel P. S. Rastrick, Camilla Bertolini, Andy Foggo, Helen Graham, Jason M. Hall‐Spencer, Marco Milazzo, Paul W. Shaw, Daniel P. Small, Pippa J. Moore

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsSt. Francis Xavier University
FundersNatural Environment Research CouncilDepartment for Environment, Food and Rural Affairs, UK Government
KeywordsOcean acidificationPopulationEcologyBiologyAdaptation (eye)Environmental scienceClimate changeDemography

Abstract

fetched live from OpenAlex

Ocean acidification is predicted to have detrimental effects on many marine organisms and ecological processes. Despite growing evidence for direct impacts on specific species, few studies have simultaneously considered the effects of ocean acidification on individuals (e.g. consequences for energy budgets and resource partitioning) and population level demographic processes. Here we show that ocean acidification increases energetic demands on gastropods resulting in altered energy allocation, i.e. reduced shell size but increased body mass. When scaled up to the population level, long-term exposure to ocean acidification altered population demography, with evidence of a reduction in the proportion of females in the population and genetic signatures of increased variance in reproductive success among individuals. Such increased variance enhances levels of short-term genetic drift which is predicted to inhibit adaptation. Our study indicates that even against a background of high gene flow, ocean acidification is driving individual- and population-level changes that will impact eco-evolutionary trajectories.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.032
GPT teacher head0.252
Teacher spread0.220 · 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

Citations52
Published2016
Admission routes1
Has abstractyes

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