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Record W2762228841 · doi:10.1063/1.5005399

Occupants’ satisfaction on building maintenance of government quarters

2017· article· en· W2762228841 on OpenAlexaboutno aff
Nur’Ain Ismail, Siti Noor Asmiza Md Ali, Nor A’aini Othman, Nooraidawati Jaffar

Bibliographic record

VenueAIP conference proceedings · 2017
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Quarter (Canadian coin)Quality (philosophy)BusinessOrder (exchange)Operations managementMarketingEngineeringFinanceGeography

Abstract

fetched live from OpenAlex

The satisfaction level of occupants toward the maintenance is very important to know the occupants comfortable with maintenance that was provided at the government quarters. The objective of the research is to determine the level of occupants satisfaction perceived of the maintenance in government quarter and also the level of quality of the maintenance of the government quarters. Data have been collected by using questionnaire in order to achieve the objective of the research. The questionnaires distributed among the occupants government quarters at Hospital Kota Bharu Kelantan. In the end of the research, the result are expected that to show the results on this satisfaction level of the occupants toward the maintenance at government quarters can be solve and the occupants can live more comfortable and get the good quality for maintenance and facilities in their houses.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0040.000

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 head0.032
GPT teacher head0.295
Teacher spread0.263 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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