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Record W2998250470

Developing a Mobile GIS Application for Facilitating Information Communications in Agri-Environmental Programs

2019· dissertation· en· W2998250470 on OpenAlexaboutno aff
Mostafa Ghiyasvand

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typedissertation
Languageen
FieldComputer Science
TopicMobile and Web Applications
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceEnvironmental planningEnvironmental resource managementGeographyData scienceEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

In recent decades, information communication has been identified as an important factor facilitating farmers to adopt agricultural beneficial or best management practices (BMPs) to improve water quality. The purpose of the study is to develop a mobile GIS application to facilitate communications of BMP economic cost and environmental effectiveness information in agri-environmental programs. At the first step, a framework was developed to define the required information content and how information should be produced and disseminated. Second, the framework was implemented to develop an open-source mobile GIS application for Android operating systems with the Gully Creek watershed of southern Ontario as the case study area. The system or interface has two main modules: One is to examine the cost and effectiveness of user-defined BMP exploratory scenarios and the other is to define BMP policy/management scenarios in which optimized BMP types and locations are identified based on environmental targets or financial constraints.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.019
GPT teacher head0.242
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

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