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Record W2140135707 · doi:10.5539/cis.v4n3p131

WebGIS to Managing Natural Resource: Case of Flooded Pasture in Lake Débo and Walado Débo

2011· article· en· W2140135707 on OpenAlexvenueno aff
Kone Forokoro, Zhong Xie

Bibliographic record

VenueComputer and Information Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMediterranean and Iberian flora and fauna
Canadian institutionsnot available
Fundersnot available
KeywordsGeographic information systemComputer scienceThe InternetAM/FM/GISWorld Wide WebSpatial analysisResource (disambiguation)Natural resourceSpatial data infrastructureDistributed GISGIS and public healthDatabaseEnvironmental resource managementGIS applicationsRemote sensingGeographyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Lake Débo and Walado Débo, one of the major Sahelian wetlands is located in Inner Delta (Mali). Given the environmental and community interest in this wetland, there is urgent need to share spatial data on natural resources. Most of the covered information is published in (internal) reports with a limited distribution. With the advent of GIS and Internet technologies, the conventional intricacies to get solutions in time and position have been improved. The combination of Web technologies and the power of GIS software enable natural resource managers to analyze GIS data that resides across the Internet. This paper is based on the design and architecture of a Web-based GIS to managing flooded pastures. MapGIS IGS (MapGIS Internet Server) is used to provide a user-friendly GIS front-end for natural resource managers and public users to perform routine GIS functions on geographic data that are distributed across the Internet. Internet based geographical data services involve management spatial data. Geographic Information System (GIS) is an indispensable tool for analyzing and managing spatial data.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.120

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.206
Teacher spread0.189 · 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 designOther design
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

Citations2
Published2011
Admission routes1
Has abstractyes

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