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Record W7005390190

Purchasing the Web: an Agent based E-retail System with Multilingual Knowledge

2003· article· en· W7005390190 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Research Information System (Università degli Studi di Brescia) · 2003
Typearticle
Languageen
FieldComputer Science
TopicMulti-Agent Systems and Negotiation
Canadian institutionsnot available
FundersDirectorate for Computer and Information Science and EngineeringNational Natural Science Foundation of ChinaChongqing University of Posts and TelecommunicationsDivision of Information and Intelligent SystemsUniversity of Notre DameChongqing UniversityNational Science Foundation
KeywordsPurchasingInformation systemQuality (philosophy)Knowledge-based systemsField (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

The workshop on Applications, Products and Services of Web-based Support Systems is held on October 13, 2003 at Halifax, Canada.It aims to a particular field of Web Intelligence by providing a forum for the discussion and exchange of ideas and information by researchers, students, and professionals on the issues and challenges brought on by the Web technology for various support systems.One of our goals is to find out how applications and adaptations of existing methodologies on the Web platform benefit our decision-makings and various activities.We are quite pleased with the quality and diversity of the accepted papers.Although this is the first workshop on this topic, we can see the acceptance of the theme by the public through the submissions.The first CFP was sent out on July 12, 2003.Within less than two months time, we received more than 40 submissions.The distribution of authors spans a large geographic area.Here are the names of more than a dozens countries and regions: Australia, Brazil, Canada, China, Czech Republic, Denmark, France, Germany, Hong Kong,

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.009

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 head0.103
GPT teacher head0.315
Teacher spread0.213 · 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 designSimulation or modeling
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
Published2003
Admission routes1
Has abstractyes

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