MétaCan
Menu
Back to cohort
Record W2418295298

A Participatory Approach to Development of a Decision Support Tool

2004· article· en· W2418295298 on OpenAlexaboutno aff
Stacy Langsdale, Barbara J. Lence, Jeff Carmichael, Stewart Cohen

Bibliographic record

VenueCritical Transitions in Water and Environmental Resources Management · 2004
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Citizen journalismEnvironmental planningProcess managementDecision support systemWater resourcesManagement scienceBusinessKnowledge managementPolitical scienceEnvironmental resource managementComputer scienceEngineeringGeographyEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

Effective decision-making in water management must consider both the physical characteristics of the system and the social, political, and institutional aspects. These latter aspects cannot be understood through scientific assessment, but are familiar to local residents and water interests. A decision support tool will be developed to assist in long-term water resources planning activities in the Okanagan Basin in British Columbia, Canada. The model will be created in a system dynamics platform, and will integrate technical hydrologic and climate change model results with institutional and social aspects. An advisory committee of local planners and decision makers will play an important role in the development of the model; they will provide information for the institutional and social aspects, and they will help to discern what level of complexity will provide the best results for their planning activities. This close involvement with local experts will ensure that the completed model will be useful for the community. Furthermore, the model development process itself will be a format for shared learning about water management in the Okanagan.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.528

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCritical Transitions in Water and Environmental Resources ManagementSame topicWater resources management and optimizationFrench-language works237,207