MétaCan
Menu
Back to cohort
Record W2153130957 · doi:10.1371/journal.pone.0004929

Producer Nutritional Quality Controls Ecosystem Trophic Structure

2009· article· en· W2153130957 on OpenAlexaff
Just Cebrián, Jonathan B. Shurin, Elizabeth T. Borer, Bradley J. Cardinale, Jacqueline T. Ngai, Melinda D. Smith, William F. Fagan

Bibliographic record

VenuePLoS ONE · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation
KeywordsTrophic levelEcosystemBiomass (ecology)Terrestrial ecosystemPrimary producersEcologyTrophic cascadeAbundance (ecology)Environmental scienceProductivityTrophic state indexMesopredator release hypothesisBiologyFood webNutrientApex predatorEutrophication

Abstract

fetched live from OpenAlex

Trophic structure, or the distribution of biomass among producers and consumers, determines key ecosystem values, such as the abundance of infectious, harvestable or conservation target species, and the storage and cycling of carbon and nutrients. There has been much debate on what controls ecosystem trophic structure, yet the answer is still elusive. Here we show that the nutritional quality of primary producers controls the trophic structure of ecosystems. By increasing the efficiency of trophic transfer, higher producer nutritional quality results in steeper ecosystem trophic structure, and those changes are more pronounced in terrestrial than in aquatic ecosystems probably due to the more stringent nutritional limitation of terrestrial herbivores. These results explain why ecosystems composed of highly nutritional primary producers feature high consumer productivity, fast energy recycling, and reduced carbon accumulation. Anthropogenic changes in producer nutritional quality, via changes in trophic structure, may alter the values and functions of ecosystems, and those alterations may be more important in terrestrial ecosystems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.996

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)0.0050.001

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 head0.024
GPT teacher head0.234
Teacher spread0.210 · 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

Citations141
Published2009
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONESame topicIsotope Analysis in EcologyFrench-language works237,207