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Record W2340411707

A Survey of End-Users' Level of Service Requirements for Snow Control for Parking Lots and Sidewalks at Different Establishments in Canada

2016· article· en· W2340411707 on OpenAlexaboutno aff
Matthew Muresan, Shahadat Hossain, Liping Fu, Brenton Law

Bibliographic record

VenueTransportation Research Board 95th Annual MeetingTransportation Research Board · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessService (business)Control (management)MarketingTransport engineeringEnvironmental economicsEngineeringComputer scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the results from a survey conducted to investigate level of service expectations for parking lots and sidewalks. Survey respondents were asked a number of questions relating to their winter maintenance expectations and their willingness to pay for higher maintenance standards. Questions were designed with realistic scenarios in mind, and aimed to uncover three aspects of public opinion towards winter maintenance: level of service expectations, opinions on the effect of winter maintenance on the environment, and willingness to contribute financially to sustainable alternatives. The collected data is useful in understanding the attitudes of business customers and the general public toward winter maintenance and its effect on the environment, infrastructure, society and economy. These results can be used for further research investigating new technologies, methods for winter maintenance practices, or developing practical guidelines to supplement existing practices. Municipalities and businesses can also use these results to evaluate the benefits of modifying their winter maintenance practices from a user-based perspective.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.108
GPT teacher head0.347
Teacher spread0.239 · 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.

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

Citations1
Published2016
Admission routes1
Has abstractyes

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