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Record W2972156976 · doi:10.1042/etls20190026

Toward long-lasting artificial cells that better mimic natural living cells

2019· article· en· W2972156976 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

VenueEmerging Topics in Life Sciences · 2019
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Alberta
FundersGiovanni Armenise-Harvard FoundationSimons Foundation
KeywordsArtificial cellSynthetic biologyArtificial lifeLiving systemsChemical communicationNatural (archaeology)Computer sciencePopulationArtificial intelligenceBiologyBiochemical engineeringEcologyEngineeringComputational biologyBiochemistry

Abstract

fetched live from OpenAlex

Chemical communication is ubiquitous in biology, and so efforts in building convincing cellular mimics must consider how cells behave on a population level. Simple model systems have been built in the laboratory that show communication between different artificial cells and artificial cells with natural, living cells. Examples include artificial cells that depend on purely abiological components and artificial cells built from biological components and are driven by biological mechanisms. However, an artificial cell solely built to communicate chemically without carrying the machinery needed for self-preservation cannot remain active for long periods of time. What is needed is to begin integrating the pathways required for chemical communication with metabolic-like chemistry so that robust artificial systems can be built that better inform biology and aid in the generation of new technologies.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.087
GPT teacher head0.332
Teacher spread0.245 · 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