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Record W2160818668 · doi:10.1139/f07-023

Spatial and temporal patterns in the food web structure of a large floodplain river assessed using stable isotopes

2007· article· en· W2160818668 on OpenAlexvenueno aff
Brian R. Herwig, David H. Wahl, John M. Dettmers, Daniel A. Soluk

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFood webFloodplainEnvironmental scienceStable isotope ratioEcologyFood chainIsotope analysisEcosystemIsotopes of carbonSpatial variabilityResource (disambiguation)Primary producersNutrientPhytoplanktonTotal organic carbonBiology

Abstract

fetched live from OpenAlex

We assessed naturally occurring stable isotope ratios of carbon (δ 13 C) and nitrogen (δ 15 N) for available food resources and consumers in the mainstream channel of the Mississippi River. Isotopic ratios were assessed for organic sources and organisms at two different sites during a fall, spring, and two summer seasons. Terrestrial C 4 plants did not appear to be an important carbon source for consumers in the mainstream channel. A mixing model, IsoSource, indicated that terrestrial C 3 vegetation, suspended algae, and epixylon were at times important food resources for large river consumers. Many consumer signatures fell outside the mixing polygon defined by these sources, indicating that there was a 13 C-depleted food resource for which we did not account. We could not distinguish precisely whether downstream allochthonous and autochthonous carbon, or in situ production, was the dominant food resource supporting consumers in these systems. However, our data suggest that in situ organic matter sources can be important. Consumer δ 13 C and δ 15 N signatures intermediate between several sources indicated widespread omnivory in the river reaches that we studied. To fully understand food web structure and energy sources in complex large river ecosystems, an integrative approach that combines related empirical data sets is needed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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

Citations41
Published2007
Admission routes1
Has abstractyes

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