MétaCan
Menu
← Back to cohort
Record W2516008041 · doi:10.1139/cjfas-2016-0197

Separating oil from water: suspension-feeding goldfish ingest liquid vegetable oil

2016· article· en· W2516008041 on OpenAlexvenueno aff
Kristin M. Edwards, Gary W. Rice, S. Laurie Sanderson

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCanolaSuspension (topology)Carassius auratusOil dropletFood scienceFish oilChemistryFish <Actinopterygii>ChromatographyFiltration (mathematics)BiologyFisheryEmulsionBiochemistry

Abstract

fetched live from OpenAlex

We show that goldfish (Carassius auratus) voluntarily ingest liquid canola oil at the surface of the water and can swallow significant quantities of oil. The ability of fish to separate floating oil from water has not been tested previously, and the mechanisms used to retain oil in the form of suspended droplets, globules, or a surface film are unknown. Chromatograms of fatty acid methyl esters (FAMEs) prepared from gut samples confirmed that goldfish were able to obtain a substantial proportion of their daily lipid intake from canola oil at the surface of laboratory aquaria. Quantification of goldfish suspension-feeding, processing, and spitting behavior suggested that upper jaw protrusion with a closed mouth during processing was important for the handling of different food types, including oil. Crossflow filtration and the generation of vortices could be involved in oil retention by goldfish, as these processes are used industrially to separate oil from water. These results have implications for the uptake of hydrophobic pollutants and dietary lipids at the surface by suspension-feeding fishes.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0000.001
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 head0.023
GPT teacher head0.205
Teacher spread0.182 · 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 designBench or experimental
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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicAquaculture Nutrition and Growth→French-language works237,207→