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Record W4364378272 · doi:10.18280/ijsdp.180309

Environmental Sustainability and Food Safety of the Practice of Urban Agriculture in Great Bandung

2023· article· en· W4364378272 on OpenAlexvenueno aff
Sunardi Sunardi, Ismail Ghulam, Nadia Istiqomah, Kabul Fadilah, Kinanti Indah Safitri, Oekan S. Abdoellah

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
FundersUniversitas Padjadjaran
KeywordsSustainabilityAgricultureUrban agricultureEnvironmental planningFood safetySustainable agricultureBusinessAgricultural economicsEnvironmental scienceGeographyEconomicsFood science

Abstract

fetched live from OpenAlex

Urban farming activities is markedly increasing with expectation to strengthen the food security in the cities, however, the crop safety and the environmental sustainability of farming activities is questioned.To address environmental sustainability, examination of river water quality and pesticide residue both in the water and crop production in periurban areas of Great Bandung were carried out.STORET index were used to identify the quality of water source with reference to Indonesia Ministry of Environment as well as the biodiversity index to ensure the health of aquatic ecosystem.The results showed that the urban farming practices had utilized clean and uncontaminated water sources to irrigate the land.The good quality of water sources could be maintained even after urban farming activities.The diversity of plankton and macrozoobenthos were relatively increasing, with low-medium level of diversity (H' index = 0.3 -2.9), even in the downstream areas after farming land.None of pesticide residues were found in the crop products.The crops from peri-urban farming of the Great Bandung were safely consumed.Meanwhile, the sustainability of urban farming in the peripheral areas of Great Bandung might be lasted so far as the upstream river pollution from the city could be well-maintained.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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 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

Citations11
Published2023
Admission routes1
Has abstractyes

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