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Record W4317532103 · doi:10.1002/lob.10544

Benthic Invertebrates on the Move: A Tale of Ocean Warming and Sediment Carbon Storage

2023· article· en· W4317532103 on OpenAlexaff
Thomas S. Bianchi, Craig J. Brown, Paul V. R. Snelgrove, Ryan R. E. Stanley, David Côté, Corey J. Morris

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans CanadaBedford Institute of OceanographyMemorial University of NewfoundlandDalhousie University
FundersUniversity of Florida
KeywordsEnvironmental scienceBenthic zoneSeafloor spreadingOceanographySedimentClimate changeSeabedFishingEffects of global warming on oceansMarine protected areaMegafaunaInvertebrateEcologyGlobal warmingFisheryHabitatGeologyBiologyPaleontology

Abstract

fetched live from OpenAlex

Abstract Ongoing effects of climate change create a dual challenge of shifting distributions of organisms and concerns about the fate of organic carbon in nature. Marine sediments store vast amounts of organic carbon, but the fate of that material hinges on the biology of organisms associated with the seafloor and how they influence rates of carbon decomposition and burial. Shifts in large megafauna that disturb and thus help to oxygenate sediments could have important ramifications regarding whether sediments release or store carbon for long periods of time. We consider snow crab and lobster, two commercially important seafloor species in the Northeastern Atlantic, and the potential effects of ongoing changes in their distributions and that of their fisheries for future climate scenarios. These ongoing biogeographic shifts, considered in tandem with areas of seabed legislatively protected from fishing impacts and thus not confounded by cumulative effects of fishing gear disturbance, offer an opportunity to study how newly arrived species that disturb large areas of the seafloor might influence the global carbon cycle.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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