MétaCan
Menu
Back to cohort
Record W2982626335 · doi:10.1109/wcnc.2019.8885903

Impact of Population on the Mutual Information of Action Potential Driven Communication in Plants

2019· article· en· W2982626335 on OpenAlexaff
Hamdan Awan, Raviraj Adve, Nigel Wallbridge, Carrol Plummer, Andrew W. Eckford

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsMolecular communicationMutual informationComputer sciencePopulationInformation transferNetwork topologyBiological systemInformation theorySIGNAL (programming language)Topology (electrical circuits)Distributed computingTelecommunicationsComputer networkArtificial intelligenceMathematicsChannel (broadcasting)EngineeringBiologyElectrical engineering

Abstract

fetched live from OpenAlex

This paper considers an electro-chemical signal based model for inter-cellular communication in plants. The input signal, composed of fast moving charged molecules is driven by an action potential (AP). APs, generated by an external stimulus, are part of the communication mechanism in plants. We extend the simple model for AP generation presented in previous work to incorporate the AP signal arriving from neighboring cells. Furthermore in this model we study the transfer of information between cells via fast moving ions. Unlike previous work, this paper does not consider diffusion but only reactions between molecules at each step. We then use an information-theoretic analysis to compute the mutual information between the input and output of this system. The key aim is to study the impact of an increase in population of cells on the mutual information. We calculate the mutual information for a large group of cells (up to 100) in three different topologies i.e., parallel, series and mixed. Finally we study the impact of a single AP on multiple cells in the system.

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 categoriesnone
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 score0.397
Threshold uncertainty score0.139

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicMolecular Communication and NanonetworksFrench-language works237,207