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Record W2096847040 · doi:10.1109/wescan.1993.270577

A sensor to characterize particles for the control and monitoring of agricultural or mining processes

2002· article· en· W2096847040 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
TopicSensor Technology and Measurement Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsVibrationParticle (ecology)Pulse (music)Elastic modulusTransducerRADIUSAcousticsParticle sizeComputer scienceBiological systemMaterials sciencePhysicsEngineeringDetectorComposite material

Abstract

fetched live from OpenAlex

The authors introduce a model of a sensor system that can form the basis to measure particle characteristics by analyzing the vibrations caused by their elastic impact upon a thin plate. In this simple sensor model, the impacting particle generates a force pulse that has its shape influenced by the particle radius, density, impact velocity and modulus of elasticity. This force pulse can be estimated by analyzing the plate vibrations as measured by the transducer. A sensor based on this model could provide a number of particle characteristics in real time, and determine size distribution or perform particle discrimination by having a sample of the particle impact upon the sensor. The factors determining the generation of the force pulse and the thin plate's response to this pulse are discussed. A system model and analysis is then reviewed. Finally some considerations in the design of a sensor are examined.< <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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.119

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 head0.072
GPT teacher head0.248
Teacher spread0.176 · 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