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Record W2128559788 · doi:10.2983/035.032.0229

Profiles of Biochemical Tracers in Unionid Mussels Across a Broad Geographical Range

2013· article· en· W2128559788 on OpenAlexaff
Teresa J. Newton, Caryn C. Vaughn, Daniel E. Spooner, Susan J. Nichols, Michael T. Arts

Bibliographic record

VenueJournal of Shellfish Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsBiologyTrophic levelMusselEcologyHabitatRange (aeronautics)EcosystemEcosystem engineerDetritusFishery

Abstract

fetched live from OpenAlex

As large, long-lived filter feeders with high clearance rates, unionid mussels are capable of altering nutrient cycling in freshwater food webs. Because the effects of mussel communities on ecosystem processes result largely from their feeding behavior, we need to understand how they sample and process particulate matter. Our objective was to determine whether measuring a suite of biochemical markers in unionid mussels, from different populations of 2 species (Amblema plicata, Actinonaias ligamentina) across a broad geographical range (4 North American rivers), would help identify their primary dietary sources. Stable isotope data were able to differentiate mussels among rivers, and sometimes species. Data on δ15N revealed that mussels fed across trophic levels but that diet varied with habitat and perhaps species. Although mussels contained the same types of fatty acids regardless of river or species, we nonetheless observed considerable variation in essential fatty acid content across rivers. Essential fatty acid profiles were dominated consistently by arachidonic acid. Source-specific fatty acid biomarkers suggested that bacterial and detrital resources might be as important as algae in many habitats. In summary, biochemical metrics differed more among rivers than between species. This suggests that mussels are either considerably adaptable in their dietary requirements or that they feed nonselectively. Understanding nutritional profiles in mussels will help restore unionid communities, which may lead to the reestablishment of their critical role as nutrient recyclers in freshwater 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)0.003
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 score0.099
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.040
GPT teacher head0.346
Teacher spread0.306 · 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

Citations28
Published2013
Admission routes1
Has abstractyes

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