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Cross-ecosystem differences in stability and the principle of energy flux

2011· review· en· W1915659949 on OpenAlexafffund
Jason M. K. Rip, Kevin S. McCann

Bibliographic record

VenueEcology Letters · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsUniversity of Guelph
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsEcosystemBiomass (ecology)Aquatic ecosystemEcologyTerrestrial ecosystemEcological stabilityAllometryFlux (metallurgy)PopulationEnvironmental scienceAtmospheric sciencesBiologyPhysicsChemistry

Abstract

fetched live from OpenAlex

Here, we review consumer-resource (C-R) theory to show that the paradox of enrichment is a special case of a more general theoretical result. That is, we show that increased energy flux, relative to the consumer loss rate, makes C-R interactions top heavy (i.e., greater C:R biomass ratio) and less stable. We then review the literature on the attributes of aquatic and terrestrial ecosystems to argue that empirical estimates of parameters governing energy flux find that aquatic ecosystems have higher rates of relative energy flux than terrestrial ecosystems. Consistent with theory, we then review empirical work that shows aquatic ecosystems have greater herbivore:plant biomass ratios while we produce novel data to show that aquatic ecosystems have greater variability in population dynamics than their terrestrial counterparts. We end by arguing that theory, allometric relationships and a significant, negative correlation between body size and population variability suggest that these results may be driven by the smaller average body sizes of aquatic organisms relative to terrestrial organisms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.275
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations164
Published2011
Admission routes2
Has abstractyes

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