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Record W2495622200 · doi:10.1017/cbo9781139149693.004

Molecular communication in biological systems

2013· book-chapter· en· W2495622200 on OpenAlexaff
Tadashi Nakano, Andrew W. Eckford, Tokuko Haraguchi

Bibliographic record

VenueCambridge University Press eBooks · 2013
Typebook-chapter
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsYork University
Fundersnot available
KeywordsMolecular communicationPerspective (graphical)Communications systemScale (ratio)BiologyMode (computer interface)NanotechnologyComputer scienceComputational biologyPhysicsHuman–computer interactionTelecommunicationsMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Molecular communication occurs ubiquitously at all levels of biological systems including molecule, cell, tissue, and organ levels. In this chapter, we examine how bionanomachines or a system of bio-nanomachines communicate using molecules. First, we introduce two dimensions to characterize molecular communication systems: scale and mode. The scale refers to the range of distances over which bio-nanomachines communicate by propagating molecules, and it is roughly divided into intracellular, intercellular, and inter-organ levels. The mode of molecular communication systems refers to how molecules propagate between bio-nanomachines; it is either passively or actively. We then go over, from a communication engineering perspective, a number of examples of molecular communication systems found in nature. Following the previous chapter, basic biology terms are in bold in this chapter.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.985
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 science0.0010.000
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 head0.021
GPT teacher head0.178
Teacher spread0.157 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2013
Admission routes1
Has abstractyes

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