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Record W2160628022 · doi:10.1139/f05-169

Food web reliance on allochthonous carbon in two high mountain lakes with contrasting catchments: a stable isotope approach

2005· article· en· W2160628022 on OpenAlexvenueno aff
Elvira Pulido‐Villena, Isabel Reche, Rafael Morales‐Baquero

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersNatural Environment Research Council
KeywordsFood webDissolved organic carbonIsotopes of carbonIsotopic signatureEnvironmental scienceδ13CParticulatesOrganic matterCarbon fibersδ15NEnvironmental chemistryPhytoplanktonTotal organic carbonIsotope analysisEcologyStable isotope ratioEcosystemChemistryNutrientBiology

Abstract

fetched live from OpenAlex

The carbon isotopic signature (δ 13 C) of dissolved inorganic carbon and food web components was examined in two high mountain lakes. Río Seco Lake is partially surrounded by alpine meadows and has temporal inlets, whereas La Caldera Lake is located on rocky terrain and does not receive inputs from runoff. We assessed whether these contrasting catchments involve differences in the isotopic signature of the food web components and then in the reliance on terrestrial carbon. The δ 13 C of dissolved inorganic carbon was not significantly different between lakes and reflected an atmospheric gas exchange origin. Unexpectedly, bulk particulate organic matter showed enriched δ 13 C values in both lakes, suggesting a terrestrial vegetation influence. Bulk particulate organic matter was exploited mostly by the cladoceran Daphnia pulicaria, whereas the copepod Mixodiaptomus laciniatus was 13 C depleted relative to particulate organic matter, indicating a selective feeding on an isotopically lighter source, likely phytoplankton. The results obtained show that, despite contrasting catchments, the food web of both lakes might be partially supported by terrestrial carbon for which utilization is species specific.

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 score0.433
Threshold uncertainty score0.989

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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

Citations46
Published2005
Admission routes1
Has abstractyes

Explore more

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