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Record W4400336017 · doi:10.1002/ece3.11702

Dilute concentrations of maritime fuel can modify sediment reworking activity of high‐latitude marine invertebrates

2024· article· en· W4400336017 on OpenAlexaff
Thomas J. Williams, David Blockley, Andrew B. Cundy, Jasmin A. Godbold, Rebecca Howman, Martin Solan

Bibliographic record

VenueEcology and Evolution · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversité Laval
FundersNatural Environment Research Council
KeywordsEnvironmental scienceClimate changeMarine ecosystemSedimentEcosystemRange (aeronautics)InvertebrateMarine invertebratesLatitudeOceanographyEcologyGeographyGeologyBiology

Abstract

fetched live from OpenAlex

Multiple expressions of climate change, in particular warming-induced reductions in the type, extent and thickness of sea ice, are opening access and providing new viable development opportunities in high-latitude regions. Coastal margins are facing these challenges, but the vulnerability of species and ecosystems to the effects of fuel contamination associated with increased maritime traffic is largely unknown. Here, we show that low concentrations of the water-accommodated fraction of marine fuel oil, representative of a dilute fuel oil spill, can alter functionally important aspects of the behaviour of sediment-dwelling invertebrates. We find that the response to contamination is species specific, but that the range in response among individuals is modified by increasing fuel concentrations. Our study provides evidence that species responses to novel and/or unprecedented levels of anthropogenic activity associated with the opening up of high-latitude regions can have substantive ecological effects, even when human impacts are at, or below, commonly accepted safe thresholds. These secondary responses are often overlooked in broad-scale environmental assessments and marine planning yet, critically, they may act as an early warning signal for impending and more pronounced ecological transitions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.516

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 studies0.0000.000
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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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