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Record W1979270053 · doi:10.2495/dne-v4-n2-143-153

Honey and sugar as surrogate products: an emergy evaluation

2009· article· en· W1979270053 on OpenAlexvenueno aff
E. Simoncini, Federica Coppola, S. Borsa, Federico Maria Pulselli

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Ecological Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureEnvironmental scienceSustainabilitySugar beetProduction (economics)Natural resource economicsEnvironmental pollutionSugarNatural resourceEnvironmental protectionBusinessAgricultural engineeringEngineeringAgronomyEconomicsEcologyBiology

Abstract

fetched live from OpenAlex

Exploitation of natural resources has reached an unsustainable level, due to the enormous growth of world population.Industrialized intensive agriculture, in particular, demands a great quantity of natural resources.White sugar is a widespread agricultural product.Its production from sugar beet or sugarcane is very expensive from the point of view of resource exploitation and sustainability.The aim of this paper is to compare white sugar and honey as sweeteners.We compared both processes of production in terms of emergy in order to establish the environmental costs and benefits of both.Transformities of honey and sugar were calculated per unit product and per unit area of land.Honey was found to have a better environmental performance than sugar production, due to the low quantity of non-renewable resources required.The environmental loading ratio indicated that honey production is more environmentally friendly than sugar production.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.012
GPT teacher head0.272
Teacher spread0.261 · 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 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

Citations2
Published2009
Admission routes1
Has abstractyes

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