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Record W4388671260 · doi:10.1101/2023.11.10.566654

A mathematical model of whole-body potassium regulation: Global parameter sensitivity analysis <sup>*</sup>

2023· preprint· en· W4388671260 on OpenAlexaff
Melissa M. Stadt, Anita T. Layton

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSensitivity (control systems)PotassiumFunction (biology)Transient (computer programming)Steady state (chemistry)Biological systemElectrolytePotassium channelMathematicsComputer scienceChemistryBiophysicsBiologyEngineeringCell biology

Abstract

fetched live from OpenAlex

Abstract Potassium (K + ) is an essential electrolyte that is tightly regulated by various complex physiological mechanisms. In this study, we analyze a mathematical model of whole-body K + regulation to investigate the sensitivity of different model outcomes to parameter values. We used the Morris method, a global sensitivity analysis technique, to evaluate the impact of the parameters on both steady state results and transient simulations during a single-meal. Our results shows that the most influential parameters and processes depend on what you are measuring. Specifically, steady state results relied primarily on parameters that were involved in kidney function, while transient results relied on hormonal feedback mechanisms. This study shows that our mathematical model of whole-body potassium regulation captures known physiological function of potassium regulation despite a large number of uncertain parameters. MSC codes 68Q25, 68R10, 68U05

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)0.006
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.064
GPT teacher head0.287
Teacher spread0.223 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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