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Record W1968359804 · doi:10.1109/icc.2014.6883880

Diffusive molecular communication with disruptive flows

2014· preprint· en· W1968359804 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMolecular communicationDetectorDiffusionFlow (mathematics)Transmission (telecommunications)AdvectionInformation flowMechanicsFunction (biology)Information transmissionStatistical physicsComputer sciencePhysicsTelecommunicationsChannel (broadcasting)Thermodynamics

Abstract

fetched live from OpenAlex

In this paper, we study the performance of detectors in a diffusive molecular communication environment where steady uniform flow is present. We derive the expected number of information molecules to be observed in a passive spherical receiver, and determine the impact of flow on the assumption that the concentration of molecules throughout the receiver is uniform. Simulation results show the impact of advection on detector performance as a function of the flow's magnitude and direction. We highlight that there are disruptive flows, i.e., flows that are not in the direction of information transmission, that lead to an improvement in detector performance as long as the disruptive flow does not dominate diffusion and sufficient samples are taken.

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 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 score0.933
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 science0.0010.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Quick stats

Citations28
Published2014
Admission routes1
Has abstractyes

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