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Record W2376342492

An Architecture of Geospatial Sensor Network for Water Quality Monitoring

2011· article· en· W2376342492 on OpenAlexaboutno aff
HE Jin-xin

Bibliographic record

VenueJournal of Jilin University · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGeospatial analysisComputer scienceWireless sensor networkSensor webSoftware architectureService-oriented architectureArchitectureNetwork architectureNetwork managementThe InternetSoftwareReal-time computingDatabaseComputer networkWireless networkWeb serviceRemote sensingWirelessKey distribution in wireless sensor networksTelecommunicationsOperating systemGeographyWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

A geospatial sensor network combines wireless sensor network with GIS,GPS and satellite remote sensing efficiently,but at present,it has no uniform architecture.So an architecture of geospatial sensor network oriented to water quality monitoring was proposed,and the design and implementation of data management subsystem is the main content.The software design of data management subsystem was based on service oriented architecture(SOA),since the monitored data is distributed,multiple sourced,and heterogeneous.Rich internet application(RIA) was the main developing mode in the software,so it has auto update,loading balance,and some other advantages.Compared with the traditional water quality monitoring approaches by the application in Ontario,Canada,this architecture can monitor more data,and visualize them more directly.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.259
Teacher spread0.209 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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