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Record W2938436784 · doi:10.5539/jas.v11n5p161

Registration and Georeferencing of the Family Farming Production Chain in Itapúa Department, Paraguay

2019· article· en· W2938436784 on OpenAlexvenueno aff
Hector T. Roos, Fábio Soares Pires, Ênio Giotto, Bruna Dalcin Pimenta, Enrique Oswin Hahn Villalba

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsGeoreferenceBusinessAgricultureAndroid (operating system)Government (linguistics)Production (economics)Information systemSWOT analysisComputer scienceKnowledge managementWorld Wide WebMarketingGeographyEngineering

Abstract

fetched live from OpenAlex

This work aimed to develop two computerized systems, modeled to record and provide information about family farming activities within Itapúa Department-Paraguay. Meetings were held with Government members and municipal technicians to obtain all the requirements and to gather all the details that are relevant for the development of the application. Thus, with all the available information, it was possible to organize and structure the two proposed systems to collect data on rural producers, their properties, their production, family composition, livestock production, processed products, infrastructure, input use, etc. As a result an application for Android called C7-SustenLAF and a Web system was developed, where the main application’s function is to carry out the data registration of the rural producers, thus providing georeferenced data, in turn the Web system has the purpose of generating and structuring the information as well as enable data retrieval by giving a series of filter options for different characteristics that are registered from the application. Thus, it is expected that these systems, used specifically to give information, can help the Government of Itapúa - Paraguay to take strategic actions and give support to the Family Farming field, contributing to better management and technical assistance to producers.

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 score0.032
Threshold uncertainty score0.135

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.200
Teacher spread0.177 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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