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Record W2417559263 · doi:10.19136/era.a3n8.676

Trophic circulation in ecosystems

2016· article· es· W2417559263 on OpenAlexaff
Manuel J. Zetina‐Rejón, Nancy J. Brown‐Peterson, Pablo del Monte‐Luna, Víctor Hugo Cruz‐Escalona, Mark S. Peterson

Bibliographic record

VenueRedalyc (Universidad Autónoma del Estado de México) · 2016
Typearticle
Languagees
FieldEnvironmental Science
TopicSustainability and Ecological Systems Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTrophic levelEcosystemEcologyEnvironmental scienceEnergy flowEcological networkEcosystem modelEcosystem managementEnvironmental resource managementBiologyEnergy (signal processing)Physics

Abstract

fetched live from OpenAlex

Abstract: The trophic aspect of ecology has been studied for more than six decades; in this contribution we address theoretical aspects relative to the concept of trophic flow at the ecosystem level and from the thermodynamics point of view. Much of this knowledge is greatly based on the consideration that ecosystems are functional units that are worth global study. Undoubtedly, a key element in the study of ecosystems relates to energy circulation through food webs. Although trophic ecology has been a sufficiently studied topic, we have not found a formal description of the trophic flow concept. Thus, this work proposes a concept based on movements of matter and energy amongst the biological components of the ecosystem. We include Odum's universal model of energy flow to synthesize the concept of trophic flow in the ecosystem. Additionally, we analyze the ecosystem thermodynamics, we discuss the characteristics of ecosystems as open and dissipation systems, and we indicate which types of mechanisms can regulate trophic flow in ecosystem. Finally, we discuss how the study of trophic flow is helpful for characterization of ecosystems structure and function, and it is important to consider it in ecosystems-based management as well.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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