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Record W4296004425 · doi:10.3390/jmse10091290

High-Frequency Responses of the Blue Mussel (Mytilus edulis) Feeding and Ingestion Rates to Natural Diets

2022· article· en· W4296004425 on OpenAlexaff
Laura Steeves, Antonio Agüera, Ramón Filgueira, Øivind Strand, Tore Strohmeier

Bibliographic record

VenueJournal of Marine Science and Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIngestionMytilusBlue musselPopulationBiologyAnimal scienceMusselEcologyEnvironmental science

Abstract

fetched live from OpenAlex

The feeding activity of bivalves is understood to change in response to a suite of environmental conditions, including food quantity and quality. It has been hypothesized that, by varying feeding rates in response to the available diet, bivalves may be able to maintain relatively stable ingestion rates, allowing them to have constant energy uptake despite changes in food availability. The purpose of this study was to determine if the blue mussel Mytilus edulis responds to fluctuations in natural diets by changing feeding rates to maintain constant ingestion rates. Three four-day experiments were conducted to measure pumping and ingestion rates in response to natural fluctuations in food concentration (chlorophyll a). Experiments were conducted in a flow-through system over the spring season in south-western Norway. Pumping and ingestion rates were measured with high temporal resolution (every 20 min), which permitted the observation of the intra- and interindividual variability of feeding rates. Results show pumping rates varying within individuals over 4 days, and some individuals pumping on average at high rates (~5 Lh−1), and some at low (~1 Lh−1), despite being held in similar conditions. The pumping rate was generally not related to changes in food availability, and population-level ingestion rates increased with increasing food availability. These results suggest that, for this population of M. edulis, feeding rates may not vary with the available diet to produce constant ingestion over time.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.803
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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