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Record W2001319303 · doi:10.1145/638750.638771

On the Latest Development in Cognitive Informatics

2003· article· en· W2001319303 on OpenAlexaff
Yingxu Wang

Bibliographic record

VenueACM SIGSOFT Software Engineering Notes · 2003
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEngineering informaticsInformaticsBusiness informaticsComputer scienceCognitive computingCognitionData scienceHealth Administration InformaticsCognitive ergonomicsDomain (mathematical analysis)Health informaticsCognitive scienceSoftware engineeringKnowledge managementEngineeringPsychologyMedicine

Abstract

fetched live from OpenAlex

Cognitive informatics is a cutting-edge and profound interdisciplinary research area that tackles the common root problems and foundations of modern informatics, computation, software engineering, AI, and life sciences. Cognitive informatics is a new frontier that studies internal information processing mechanisms and processes of the brain, and their engineering applications in computing, software, and IT industries. The functional architecture of the brain and the natural intelligence of the mind are the last domain yet to be explored in cognitive informatics.This article reports the latest development at the First IEEE International Conference on Cognitive Informatics (ICCI '02). This report intends to draw attention of researchers, practitioners and graduate students on the investigation of cognitive mechanisms and processes of human information processing, and to stimulate the collaborative international effort on cognitive informatics research and engineering applications.

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)0.004
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.006
Scholarly communication0.0070.015
Open science0.0010.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0070.003

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 head0.022
GPT teacher head0.227
Teacher spread0.205 · 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
GenreReview

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

Citations2
Published2003
Admission routes1
Has abstractyes

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