WEB-BASED COLLABORATIVE DECISION SUPPORT SERVICES: CONCEPT, CHALLENGES AND APPLICATION
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".