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Record W4385490781 · doi:10.1016/j.cpc.2023.108884

Parallel model of chemical reactions on a grained molecular level

2023· article· en· W4385490781 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer Physics Communications · 2023
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsnot available
FundersAlliance de recherche numérique du CanadaNarodowe Centrum Nauki
KeywordsComputer scienceLattice (music)Computational scienceSimple (philosophy)Cluster (spacecraft)AlgorithmChemical reactionParallel computingField-programmable gate arrayExtension (predicate logic)Statistical physicsChemistryPhysicsComputer hardware

Abstract

fetched live from OpenAlex

In this paper, the model of chemical reactions on grained molecular level is presented. This model allows simulating simple chemical reactions of first- and second-order using the parallel approach. Proposed model has been implemented as an extension to the Dynamic Lattice Liquid (DLL) algorithm on the FPGA cluster – ARUZ (Analyzer of Real Complex Systems). An example simulation achieves a performance of 4,792 algorithm cycles per second ,17×109 Lattice Updates Per Second (LUPS), 3.7 times faster than the GPU cluster implementation, and its result agrees very well with theory and practical experiment.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.567
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.103
GPT teacher head0.314
Teacher spread0.211 · 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