MétaCan
Menu
Back to cohort
Record W2294237627 · doi:10.5539/jas.v8n4p140

Cost-, Cumulative Energy- and Emergy Aspects of Conventional and Organic Winter Wheat (Triticum aestivum L.) Cultivation

2016· article· en· W2294237627 on OpenAlexvenueno aff
Anna Kuczuk

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Ecological Systems Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEmergyProduction (economics)Investment (military)Agricultural engineeringEnvironmental scienceOrganic farmingAgricultural scienceProduction costQuality (philosophy)Efficient energy useYield (engineering)Winter wheatAgronomyEconomicsAgricultureEcologyBiologyEngineeringMicroeconomicsSustainability

Abstract

fetched live from OpenAlex

The differences in the investment, cost, energy efficiency of cultivation in organic and conventional systems are considerable. This paper reports the results of emergy analysis and comparison of cost and energy efficiency of the two systems based on the example of growing winter wheat (Triticum aestivum L.). The differences between the two systems include the total cost of production as well as various levels of economic efficiency of production in a conventional system. It was noted that the cost of conventional production is decided on by the large cost of production materials. These farms demonstrate considerably lower energy efficiency of production. In contrast, in organic farms we can observe lower yield levels associated with the more extensive production quality. However, in the considerations we needs to take into account how the two types of production affect the natural environment. For this reason, emergy analysis was taken up, as its results indicate lower energy use in ecological cultivation.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.231
Teacher spread0.219 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicSustainability and Ecological Systems AnalysisFrench-language works237,207