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Record W3008327533 · doi:10.1002/ecy.3013

Efficiency of crustacean zooplankton in transferring allochthonous carbon in a boreal lake

2020· article· en· W3008327533 on OpenAlexafffund
Guillaume Grosbois, Dominic Vachon, Paul A. del Giorgio, Milla Rautio

Bibliographic record

VenueEcology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalUniversité du Québec à Chicoutimi
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsZooplanktonTrophic levelEnvironmental scienceBiomass (ecology)EcologyPelagic zoneContext (archaeology)Food webCarbon cyclePhytoplanktonEcosystemOceanographyBiologyNutrientGeology

Abstract

fetched live from OpenAlex

Increased incorporation of terrestrial organic matter (t-OM) into consumer biomass (allochthony) is believed to reduce growth capacity. In this study, we examined the relationship between crustacean zooplankton allochthony and production in a boreal lake that displays strong seasonal variability in t-OM inputs. Contrary to our hypotheses, we found no effect of allochthony on production at the community and the species levels. The high-frequency seasonal sampling (time-for-space) allowed for estimating the efficiency of zooplankton in converting this external carbon source to growth. From the daily t-OM inputs in the lake (57-3,027 kg C/d), the zooplankton community transferred 0.2% into biomass (0.01-2.36 kg C/d); this level was of the same magnitude as the carbon transfer efficiency for algal-derived carbon (0.4%). In the context of the boundless carbon cycle, which integrates inland waters as a biologically active component of the terrestrial landscape, the use of the time-for-space approach for the quantifying of t-OM trophic transfer efficiency by zooplankton is a critical step toward a better understanding of the effects of increasing external carbon fluxes on pelagic 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)0.000
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 score0.106
Threshold uncertainty score1.000

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)0.0010.000

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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

Citations29
Published2020
Admission routes2
Has abstractyes

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