MétaCan
Menu
Back to cohort
Record W2156302998

Scaling limits of spatial chemical reaction networks

2013· preprint· en· W2156302998 on OpenAlexaff
Peter Pfaffelhuber, Lea Popovic

Bibliographic record

VenuearXiv (Cornell University) · 2013
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsScalingBiological systemReaction dynamicsDynamics (music)Chemical reactionStatistical physicsSpatial ecologyScale (ratio)Molecular dynamicsSpatial configurationChemical speciesChemical physicsMoleculePhysicsComputer scienceChemistryDistribution (mathematics)MathematicsComputational chemistryEcologyBiologyQuantum mechanicsGeometry
DOInot available

Abstract

fetched live from OpenAlex

We study the effects of fast spatial movement of molecules on the dynamics of chemical species in a spatial heterogeneous chemical reaction network. The reaction networks we consider are either single- or multiscale. When the dynamics is on a single-scale, fast spatial movement has the single effect of averaging the dynamics over the distribution of all the species. However, when the dynamics is on multiple scales our findings show that the spatial movement of molecules has different effects depending on whether the movement of each type of species is faster or slower then the effective dynamics of the reaction system on this molecular type. We assume the reaction dynamics separates into a fast subsystem of reactions with a stable stationary probability measure and a slow subsystem on the time scale of interest. We obtain results for both the case when the fast subsystem is without and the case with conserved quantities, where a conserved quantity is a linear combination of fast species evolving on a slower timescale. 1

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0010.000
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 head0.031
GPT teacher head0.178
Teacher spread0.147 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations4
Published2013
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicGene Regulatory Network AnalysisFrench-language works237,207