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Record W845931395

Priority planning and budgeting process for municipalities

2004· article· en· W845931395 on OpenAlexaboutno aff
J J Hajek, Signe Boudreau, D K Hein, C Olidis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsCompendiumBusinessGeneral partnershipProcess (computing)Plan (archaeology)Environmental planningInvestment (military)Political scienceFinanceComputer scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

The paper provides background information on the National Guide to Sustainable Municipal Infrastructure and on the collaborative effort involved in its development. It contains a summary of the recommended procedures for priority planning and budgeting for pavement preservation that are now a part of the Guide. The Federation of Canadian Municipalities is leading the development of the National Guide in partnership with the National Research Council of Canada. The objective of the National Guide is to provide a single authoritative reference for infrastructure preservation through a compendium of technical best practices. The Guide also aims to assist municipalities and other infrastructure owners with decision-making and investment planning tools. The paper describes how the planning and budgeting process for pavement preservation can be improved by using a transparent process that effectively identifies and documents pavement preservation needs and translates them into prioritized projects and required budgets. (a) For the covering entry of this conference, please see ITRD abstract no. E212095.

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)0.036
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.066
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.061
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.010
Science and technology studies0.0050.001
Scholarly communication0.0130.006
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0190.004

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 head0.271
Teacher spread0.257 · 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 designNot applicable
Domainnot available
GenreMethods

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

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