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Record W1967659667 · doi:10.1139/z99-256

Food-web dynamics and trophic-level interactions in a multispecies community of freshwater unionids

2000· article· en· W1967659667 on OpenAlexvenueno aff
Susan J. Nichols, Donald L. Garling

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTrophic levelBiologyFood webDetritusAlgaeOmnivoreEcologyStable isotope ratioTrophic state indexPicoplanktonWater columnIsotope analysisDiatomδ15NNutrientδ13CPhytoplanktonPredation

Abstract

fetched live from OpenAlex

We compared feeding habits and trophic-level relationships of unionid species in a detritus-dominated river and an alga-dominated lake using biochemical analyses, gut contents, and stable-isotope ratios. The δ 13 C ratios for algae and other food-web components show that all unionids from both the river and the lake used bacterial carbons, not algal carbons, as their main dietary source, in spite of positive selection and concentration of diatoms and green algae from the water column in the gut and mantle cavity. Algae did provide key nutrients such as vitamins A and D and phytosterols that were bioaccumulated in the tissues of all species. The δ 15 N ratios for the multispecies unionid community in the Huron River indicated some differences in nitrogen enrichment between species, the greatest enrichment being found in Pyganadon grandis. These δ 15 N ratios indicate that unionids may not always feed as primary consumers or omnivores. Stable-isotope data were critical for delineating diets and trophic-level interactions of this group of filter-feeders. Further refinements in identifying bacterial and picoplankton components of the fine particulate organic matter are needed to complete our understanding of resource partitioning between multispecies unionid populations.

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 score0.978
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.218
Teacher spread0.199 · 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

Citations126
Published2000
Admission routes1
Has abstractyes

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