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Record W7010919423

A knowledge based expert systems based upon webgrid / Mohd Nazir Sarah

2004· other· en· W7010919423 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUiTM Institutional Repositories (Universiti Teknologi MARA) · 2004
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicValue Engineering and Management
Canadian institutionsnot available
Fundersnot available
KeywordsRepertory gridExpert systemBiddingConstruct (python library)ProcurementCognitionPerceptionKnowledge baseLegal expert systemFocus (optics)
DOInot available

Abstract

fetched live from OpenAlex

Since the construction industry still lacks a strong Information Technology (IT) approach, especially in the bidding and tendering domain, the objectives of the thesis were to study the pattern of the experts in the bidding and tendering area in selected departments of Malaysia Airlines. The focus would be those who were making decisions not only from an IT perspective, but also from sociological and psychological perspectives. Besides that, the study was also carried out to confirm whether their decision making exercise could be automated to assist and structure the process. The outcome of the study is to propose an Expert System Prototype to the experts, that contains a knowledge based system which has rules (logic program) based on their cognitive perception on how to select bidders. Fifteen experts from four different departments were involved in this study, using the Repertory Grid methodology, from where their knowledge was elicited, analyzed, represented and modeled as a rule based system, embedded in the prototype, called AdjuComm. The prototype was developed on the WebGrid-III Expert System tool(software) which originated from the University of Calgary, Canada and is freely available on the internet. Using the tool, people's views or opinions could be weighted or measured and their construct pattern could be analyzed. The finding of the study shows that the developed prototype (AdjuComm) could pattern the experts' cognitive perceptions, especially when dealing with human cognition of bids, and be able to minimize bias. The study also shows that the WebGrid-III could be used as an important tool for developing knowledge based expert systems due to its efficiency, scalability, accessibility and flexibility.As a part of the range of Knowledge Based Expert Systems, The AdjuComm could accept vague (blank) data within certain limits, and it is suggested that certain improvements can be added to the prototype in the future, based on the requirement to make it more advantageous and beneficial in the bidding domain.To ensure the efficiency and flexibility of the prototype, different categories of experts were invited to verify and give comments on the prototype. Their feedback and views were important in order to justify and enhance the prototype scalability which could be described as 'initially successful'. ETR

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.209
Teacher spread0.194 · 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