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Record W2162790870 · doi:10.1109/caia.1992.200006

Pitch Expert-an engineered collection of specialized knowledge structures

2003· article· en· W2162790870 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsComputer Research Institute of Montréal
FundersUniversidad de Especialidades Espíritu Santo
KeywordsExpert systemComputer sciencePulp (tooth)Pulp millKraft processMillDomain knowledgeKnowledge engineeringSet (abstract data type)Kraft paperArtificial intelligenceEngineeringPulp and paper industryMechanical engineeringProgramming language

Abstract

fetched live from OpenAlex

An expert system is described which diagnoses pitch related production problems in the kraft type of pulp mill. Pitch is a sticky material which forms deposits in pulp mills. Pitch Expert, a knowledge-based trouble shooter for pitch problems, operates under a heavy set of constraints imposed by the combination of the expertise itself and the operating environment of pulp mills. To satisfy these constraints, specialized structures and mechanisms were developed and refined to achieve a custom fit. These structures and constraints aid in the maintenance and continued addition of new knowledge to the systems. The utilization of this approach has led to a knowledge-based system with which both the domain expert and the end-users feel comfortable. The result is that the number of mills using this system is growing rapidly.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.024
GPT teacher head0.269
Teacher spread0.246 · 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

Quick stats

Citations2
Published2003
Admission routes1
Has abstractyes

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