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Record W4405760765 · doi:10.1002/lol2.10454

Sink or break: Oil increases resistance of phytoplankton aggregates to fragmentation

2024· article· en· W4405760765 on OpenAlexafffund
Kai Ziervogel, Julia Sweet, Yixuan Song, Laura Bretherton, Matthew J. Rau, Antonietta Quigg, Uta Passow

Bibliographic record

VenueLimnology and Oceanography Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsMount Allison University
FundersNatural Sciences and Engineering Research Council of CanadaDirectorate for GeosciencesNational Science Foundation
KeywordsMarine snowSnowEnvironmental scienceFragmentation (computing)PhytoplanktonSink (geography)OceanographyOil spillGeologyWater columnGeographyGeomorphologyEcologyEnvironmental engineeringBiology

Abstract

fetched live from OpenAlex

Abstract Fragmentation of marine snow affects the downward flux of organic matter, and other aggregate‐associated compounds such as oil. Using phytoplankton aggregates, we demonstrate that marine snow with oil, termed marine oil snow, had a higher resistance to fragmentation compared to marine snow without oil when exposed to turbulence ex situ. At moderate shear levels, typical of the ocean mixed layer, 17% of marine snow without oil broke, whereas 63% of marine snow fragmented at intermediate shear. In contrast, only 17% and 33% of marine oil snow fragmented at the intermediate and highest shear levels, respectively. Our results suggest that oil increases the cohesion and stability of aggregates making them less susceptible to breaking. This work contributes toward explaining the exceptional oil sedimentation event following the 2010 spill in Gulf of Mexico. It also enhances our understanding of the factors that determine the probability of sinking aggregates to fragment.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.316

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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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