MétaCan
Menu
Back to cohort
Record W22742215 · doi:10.1021/ac00253a705

Conflict Resolution Support System: A Software for the Resolution of Conflicts in Water Resource Management

2003· article· en· W22742215 on OpenAlexfundno aff
K. D. W. Nandalal, Slobodan P. Simonović

Bibliographic record

VenueAnalytical Chemistry · 2003
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsnot available
FundersInstitute for Catastrophic Loss Reduction
KeywordsResolution (logic)Conflict resolutionResource (disambiguation)Computer scienceConflict managementSoftwareResource management (computing)BusinessEnvironmental resource managementKnowledge managementProcess managementPolitical scienceEnvironmental scienceArtificial intelligenceDistributed computingOperating system

Abstract

fetched live from OpenAlex

Water is an important factor in conflicts among stakeholders at the local, regional, and international level. Water conflicts have taken many forms, but they almost always arise from the fact that the freshwater resources of the world are not partitioned to match the political borders, nor are they evenly distributed in space and time. Sharing a limited water resource by several stakeholders can create conflicts among them when their requirements exceed availability. In such situations, water allocation based on a traditional optimization or simulation modeling may not resolve the dispute among them due to the lack of their participation in the solution process. Direct involvement of the stakeholders in the conflict resolution process provides for a better understanding of the conflict and offers a significant opportunity for its resolution.\nA systemic approach has been taken in this research to approach resolution of conflicts over water. By helping stakeholders to explore and resolve the underlying structural causes of conflict our approach offers a significant opportunity for its resolution. We define the five main functional activities for assisting the conflict resolution process as: (i) communication; (ii) problem formulation; (iii) data gathering and information generation; (iv) information sharing; and (v) evaluation of consequences. A computerized technical support is developed in the form of the Conflict Resolution Support System (CRSS) for implementation of a systemic approach to water conflicts. The CRSS includes computational modules necessary to resolve conflicts resulting from water shortages in irrigation, drinking water supply, and hydropower generation and flood control. Its principal components include an artificial intelligence-based communication system, a database management system, and a model base management system.\nThe use of CRSS is demonstrated through its application to three types of water sharing conflicts. The CRSS is developed as a tool to assist a conflict resolution process and a tool for training stakeholders in the conflict resolution process.

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)0.003
metaresearch head score (Gemma)0.008
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: Software · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0270.009

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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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
GenreSoftware

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

Citations17
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueAnalytical ChemistrySame topicAI-based Problem Solving and PlanningFrench-language works237,207