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Record W2752926127 · doi:10.1021/acs.cgd.7b00730

Motion-Based Multiple Object Tracking of Ultrasonic-Induced Nucleation: A Case Study of <scp>l</scp>-Glutamic Acid

2017· article· en· W2752926127 on OpenAlex
Zhenguo Gao, Dan Zhu, Yuanyi Wu, Sohrab Rohani, Junbo Gong, Jingkang Wang

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCrystal Growth & Design · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCrystallization and Solubility Studies
Canadian institutionsWestern University
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsNucleationTracking (education)CrystallizationReflection (computer programming)Materials scienceUltrasonic sensorProcess (computing)Computer scienceObject (grammar)SmoothingBiological systemAcousticsComputer visionArtificial intelligencePhysicsEngineeringChemical engineeringBiologyThermodynamics

Abstract

fetched live from OpenAlex

A robust nucleation tracking technology was proposed to track the nucleation process of l -glutamic acid in this study. A motion-based multiple object tracking (MMOT) model was introduced to crystallization, for the first time, to help to track the moving crystals. A waterproof microcamera combined with a home-designed vial adaptor was used to record the nucleation process video stream. Optimization of parameters in the MMOT model and a moving average (MA) based smoothing method helped to determine the starting point of nucleation. Results showed the newly developed technology performed better under the influence of ultrasonic irradiation, which disabled the use of focused beam reflection measurement (FBRM).

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)0.001
metaresearch head score (Gemma)0.004
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 score0.086
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.063
GPT teacher head0.289
Teacher spread0.225 · 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