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Record W1998950365 · doi:10.4018/jcini.2010010101

Perspectives on Cognitive Informatics and Cognitive Computing

2010· article· en· W1998950365 on OpenAlexaff
Yingxu Wang, George Baciu, Yiyu Yao, Witold Kinsner, Keith C. C. Chan, Bo Zhang, Stuart R. Hameroff, Ning Zhong, Chu-Ren Hunag, Ben Goertzel, Duoqian Miao, Kenji Sugawara, Guoyin Wang, Jane You, Du Zhang, Haibin Zhu

Bibliographic record

VenueInternational Journal of Cognitive Informatics and Natural Intelligence · 2010
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsNipissing UniversityUniversity of ManitobaUniversity of ReginaUniversity of Calgary
Fundersnot available
KeywordsCognitive computingComputer scienceComputational intelligenceCognitionInformaticsData scienceEngineering informaticsArtificial intelligenceKnowledge representation and reasoningGranular computingCognitive scienceCognitive models of information retrievalInformation scienceHealth informaticsPsychology

Abstract

fetched live from OpenAlex

Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0030.020
Scholarly communication0.0090.009
Open science0.0010.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.297
Teacher spread0.285 · 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 designTheoretical or conceptual
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

Citations82
Published2010
Admission routes1
Has abstractyes

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