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Record W2900267120 · doi:10.1115/ipc2018-78808

CSA Z663: Land Use Planning in the Vicinity of Pipelines

2018· article· en· W2900267120 on OpenAlexaff
Ed Plant, Sue Capper

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsScope (computer science)StakeholderVariety (cybernetics)Consistency (knowledge bases)Land-use planningStakeholder engagementPipeline (software)Computer scienceLand useProcess managementProcess (computing)Environmental planningBusinessEnvironmental resource managementKnowledge managementEngineeringPolitical sciencePublic relationsCivil engineeringGeography

Abstract

fetched live from OpenAlex

There are few standards or regulations to help stakeholders consider land use and development in the vicinity of existing pipeline systems. Land use planning that considers the existence of pipeline systems can support the planning for and provision of emergency services and pipeline integrity. This approach can also promote public safety and awareness through consistent and collaborative stakeholder engagement early in the land use planning process. In 2016, a CSA workshop was held with a variety of stakeholders impacted by land use planning around pipeline systems. The workshop identified that there was a need for consistency across the jurisdictions in the form of a national standard. The main goal of the new CSA Z663 standard is to provide guidance and best practices for land use planning and development. It also addresses roles, responsibilities and engagement of all stakeholders to help establish a consistent approach to land use planning. A review of CSA Z663 will illustrate how this document provides information, guidance and tools that are inclusive to all stakeholders. This paper will also highlight the history and key drivers behind the new CSA Z663 standard and provide an overview of the current scope and content. Finally, the paper will describe future considerations and additions to the standard.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.157

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 head0.016
GPT teacher head0.245
Teacher spread0.229 · 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 designObservational
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
Published2018
Admission routes1
Has abstractyes

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