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Record W97556283

WEB-BASED COLLABORATIVE DECISION SUPPORT SERVICES: CONCEPT, CHALLENGES AND APPLICATION

2006· article· en· W97556283 on OpenAlexaff
Lei Wang, Qiuming Cheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceDecision support systemKnowledge managementWeb serviceMetadataR-CASTWorld Wide WebData scienceBusiness decision mappingData mining
DOInot available

Abstract

fetched live from OpenAlex

The complexity of spatial decision-making makes it difficult for individual organization to deal effectively with decision making. Difficulty in linking data, analysis tools and models across organization is one of the barriers to be overcome in developing integrated spatial decision-making. By facilitating an inter-organizational decision-making process through information exchange, knowledge and model sharing, collaboration can be used to resolve conflicts and reduce uncertainty. Web-based spatial decision support systems (SDSS) can increase public access and involvement in inter-organizational collaborative decision-making. However, most web-based SDSS are application-specific DSS consist of software, data, and model for a specific decision problem. Most of these systems utilize different types of Internet technologies and framework, and cannot share their data and model with each other. There are no generic tools that would accept user data online, supporting data, software and model sharing and hence act as a webbased decision support service. Therefore, it is required to develop a standardized framework for Web-based Collaborative Decision Support Services (WCDSS), supporting information exchange and knowledge, software and model sharing from different organizations on the web. Such a WCDSS supply both metadata services, geodata services and geoprocessing services to help collaborative decision-making, not only support distributed data sharing and services, but also support distributed software sharing and model sharing. This WCDSS can play an important role to establish a collaborative mechanism across organizational boundaries for spatial decision-making support. This paper will give a detail literature review analysis of WCDSS, concluding the historical route towards WCDSS and its research progress and challenges. Then using web-based weights of evidence model for flowing well prediction as a case study, a conceptual framework of WCDSS will be proposed. Based on this conceptual framework, a prototype system will be designed to support the information exchange and knowledge, software and model sharing from different organizations on the web.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.994

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 communication0.0000.000
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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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 designNot applicable
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

Citations18
Published2006
Admission routes1
Has abstractyes

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