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Record W4361189739 · doi:10.1016/j.farsys.2023.100005

Role of organic farming for achieving sustainability in agriculture

2023· article· en· W4361189739 on OpenAlexaff
Ashoka Gamage, Ruchira Gangahagedara, Jeewan Gamage, Nepali Jayasinghe, Nathasha Kodikara, Piumali Suraweera, Othmane Merah

Bibliographic record

VenueFarming System · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Guelph
FundersUniversity of PeradeniyaUniversity of Tsukuba
KeywordsAgricultureSustainabilityEcological farmingOrganic farmingIntegrated farmingBusinessSustainable agricultureProductivityNatural resource economicsEnvironmental impact of agricultureMixed farmingAgroforestryEnvironmental scienceGeographyEconomicsEcologyEconomic growth

Abstract

fetched live from OpenAlex

Agriculture and farming have a long history. Agriculture is the main economic structure for many developed and developing countries. The modern agricultural practices affect the environment namely nutrient cycle, soil erosion, carbon sequestration, and many other ecological patterns. Organic farming is influential practice to minimize the environmental and ecological impact of sustainable development. Usage of more organic matters in agricultural practices can reduce the adverse effects on the environment by keep saving its natural cycles on recovery process and organic farming may enhance the food quality too. The organic farming may largely exclude the usage of chemical fertilizers, pesticides, growth hormones and feed additives of livestock activities. A combination of organic farming and new technologies is of utmost importance to reduce the limitations and challenges of organic farming. The innovative methods and new approaches making new trends toward sustainability farming system and enhances the agricultural productivity, and quality of life of many farmers in an environmentally friendly way. In other words, organic farming mirrors the sustainability concepts of Global Agriculture.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.204
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations559
Published2023
Admission routes1
Has abstractyes

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