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Record W2317864747 · doi:10.1061/40754(183)24

A Prototype E-Society Portal: Using a Semantic Model to Represent Sustainability Aspects of Highway Projects to Local Communities

2005· article· en· W2317864747 on OpenAlexaff
Tamer E. El-Diraby, Bo Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceOntologySustainabilityCompendiumVocabularySet (abstract data type)Knowledge managementWorld Wide WebGeographyEcology

Abstract

fetched live from OpenAlex

More than any other industry, the infrastructure has a direct impact on our communities and quality of life. There is a need to utilize the compendium of information technology research and applications to investigate how to engage local communities in the design and configuration of such projects. Systems are needed to explain to communities the various elements of projects, who is sponsoring them, who has the decision powers to develop and approve such components and, more importantly, the impacts of various design options on sustainability. This paper developed an ontology for sustainability in highway construction. An ontology is a conceptual semantic model that attempts to capture human knowledge (both explicit and tacit) in a consistent manner. Ontologies include three main elements: a taxonomy (common vocabulary presented in concept trees), set of relationships (linking concepts across trees) and axioms (limitation/constraints on the behavior of concepts). The ontology was used to develop a portal for broadcasting highway design features to local communities. By browsing through the portal, a user can learn about project elements, the impacts of each element on sustainability issues, who is sponsoring such element and what efforts have been made to reduce any impacts of such elements on local communities.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.871

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 head0.031
GPT teacher head0.291
Teacher spread0.260 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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