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Record W2950749268 · doi:10.1002/lno.11202

Living the high turbidity life: The effects of total suspended solids, flow, and gill morphology on mussel feeding

2019· article· en· W2950749268 on OpenAlexafffund
Shaylah Tuttle‐Raycraft, Josef Daniel Ackerman

Bibliographic record

VenueLimnology and Oceanography · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of Guelph
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Natural Resources and Forestry
KeywordsTurbidityMusselTotal suspended solidsWater flowBiologyFlux (metallurgy)Environmental scienceAnimal scienceFisheryHydrology (agriculture)EcologyChemistryGeologySoil scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract Unionid mussels from clear‐water rivers are often found in turbid waters in which their feeding and reproductive efficiency should be impaired. We examined the feeding response of Lampsilis siliquoidea from a clear and turbid river in response to increased concentrations of total suspended solids (TSS) and water velocity in a funnel chamber and a recirculating flow chamber. Four TSS concentrations (0, 5, 20, and 100 mg L −1 ) and four velocities (0, 0.05, 0.15, and 0.25 m s −1 ) were used to create 16 experimental conditions corresponding to nine TSS flux (concentration × velocity) levels. TSS flux significantly affected clearance rates (CR); however, the relationship was complex. Increased TSS led to lower CR in mussels from clear and turbid river at all velocities; however, increases in velocity reduced the magnitude of the decline with respect to TSS. Overall, the turbid river mussels were less affected by increased TSS than those from the clear‐water rivers. The mussels from the turbid river also differed anatomically (i.e., greater palp:gill ratio; number of cilia per cirri; number of cirri cm −1 ; and thickness of palp cilia, and less cilia per unit area of palp) from the clear river animals. It is likely that these anatomical differences allow the mussels to feed more efficiently in turbid water, which explains the observed differences in CR response to TSS. These results demonstrate the importance of multistressor approaches in evaluating aquatic organisms, as well as providing a potential mechanism to explain how mussels thrive in turbid rivers.

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 score0.008
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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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

Citations35
Published2019
Admission routes2
Has abstractyes

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