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Record W2104623594 · doi:10.1139/f06-148

Efficiencies of benthic and pelagic trophic pathways in a subalpine lake

2006· article· en· W2104623594 on OpenAlexvenueno aff
M. Jake Vander Zanden, Sudeep Chandra, Sangkyu Park, Yvonne Vadeboncoeur, Charles R. Goldman

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsBenthic zonePelagic zoneTrophic levelZooplanktonEcologyPredationEnvironmental scienceForagingFisheryBiology

Abstract

fetched live from OpenAlex

Although the study of lakes has traditionally focused on pelagic production pathways, recent stable isotope and diet evidence indicates that benthic algal production is an important contributor to fish production. This has led to the suggestion that energy may be more efficiently passed along benthic food chains relative to their pelagic counterparts. To test this idea, we combined stable isotope based assessments of energy flow pathways with estimates of pelagic- and benthic-based primary and secondary production in Castle Lake, California. Approximately 50% of whole-lake primary production and 30% of whole-lake secondary production occurred in benthic habitats. Stable carbon isotopes and dietary data indicated that fish were predominantly supported by benthic (63%) and terrestrial (24%) secondary production. Ecological efficiencies (algal production / invertebrate production) were low in Castle Lake (<3%), though zoobenthic production was more efficiently passed to fish than was zooplankton production. The larger size of benthic prey relative to pelagic prey may affect fish prey selection and foraging efficiency, resulting in differences in ecological efficiency between pelagic and benthic trophic pathways.

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 score0.349
Threshold uncertainty score0.997

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 studies0.0000.002
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 head0.010
GPT teacher head0.187
Teacher spread0.176 · 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

Citations148
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicIsotope Analysis in EcologyFrench-language works237,207