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Record W2771754487 · doi:10.1038/s41467-017-02157-0

A mechanistic theory for aquatic food chain length

2017· article· en· W2771754487 on OpenAlexafffund
Colette Ward, Kevin S. McCann

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProductivityEcosystemFood chainContext (archaeology)Biomass (ecology)Environmental scienceAquatic ecosystemFlux (metallurgy)Marine ecosystemEcologyNatural resource economicsBiologyEconomicsChemistry

Abstract

fetched live from OpenAlex

Multiple hypotheses propose an ostensibly disparate array of drivers of food chain length (FCL), with contradictory support from natural settings. Here we posit that the magnitude of vertical energy flux in food webs underlies several drivers of FCL. We show that rising energy flux fuels top-heavy biomass pyramids, promoting omnivory, thereby reducing FCL. We link this theory to commonly evaluated hypotheses for environmental drivers of FCL (productivity, ecosystem size) and demonstrate that effects of these drivers should be context-dependent. We evaluate support for this theory in lake and marine ecosystems and demonstrate that ecosystem size is the most important driver of FCL in low-productivity ecosystems (positive relationship) while productivity is most important in large and high-productivity ecosystems (negative relationship). This work stands in contrast to classical hypotheses, which predict a positive effect of productivity on FCL, and may help reconcile the contradictory nature of published results for drivers of FCL.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.023
GPT teacher head0.304
Teacher spread0.282 · 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 designTheoretical or conceptual
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

Citations94
Published2017
Admission routes2
Has abstractyes

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