MétaCan
Menu
Back to cohort
Record W4392593642 · doi:10.3389/fmars.2024.1337358

Oil Droplet Capture and Ingestion by Filter-Feeding Sabellid and Serpulid Polychaetes

2024· article· en· W4392593642 on OpenAlexafffund
Katherine Beaudry, Christopher B. Cameron

Bibliographic record

VenueFrontiers in Marine Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIngestionPolychaeteFisheryBiologyOceanographyEnvironmental scienceEcologyGeographyZoologyGeologyEndocrinology

Abstract

fetched live from OpenAlex

Benthic filter-feeders form an essential role in marine food chains as they constitute the bridge between the microscopic primary producers and the consumers. Although filter-feeders mainly feed on solid particles, they also capture and ingest oil droplets. Usually, these microdroplets come from the decomposition of animals or algae or from petroleum oils that enter water via spills and leakages. Here, we used videography, TRITC fluorescence microscopy, and fluid mechanics to study the capture mechanisms of canola, fish, and four petroleum motor oil droplets by the filter feeding sabellid and a serpulid polychaetes. Schizobranchia insignis , Eudistylia vancouveri, Myxicola infundibulum and Serpula columbiana actively feed on waste motor oil droplets in seawater. A further experiment found that S. insignis fed on all types of oil droplets, demonstrating no selectivity based on type. The oil droplet capture mechanism of S. insignis were direct interception and sieving, like that of solid particles. The size range of droplets ingested was 10 to 300 µm in diameter, but these ranges differed depending on the density and viscosity of the oils. Higher density and viscous oils were captured at smaller droplet sizes. These results are the first to characterize the mechanics of oil droplet capture, transport and ingestion by benthic ciliary filter feeders, and contribute to understanding the behavior of animals in response to oil emulsions, and how oils enter marine food webs.

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 score0.609
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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

Explore more

Same venueFrontiers in Marine ScienceSame topicOil Spill Detection and MitigationFrench-language works237,207