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Record W3143323622 · doi:10.1126/science.abb3099

Imaging cell lineage with a synthetic digital recording system

2021· article· en· W3143323622 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueScience · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
FundersNational Institute of Mental HealthNational Institutes of HealthWellcome TrustCanadian Aeronautics and Space InstitutePaul G. Allen Family FoundationHoward Hughes Medical Institute
KeywordsIntegraseBiologyLineage (genetic)Computational biologyContext (archaeology)GeneGeneticsCell biology

Abstract

fetched live from OpenAlex

intMEMOIR traces cell lineages Cell lineage plays a pivotal role in cell fate determination. Chow et al. demonstrate the use of an integrase-based synthetic barcode system called intMEMOIR, which uses the serine integrase Bxb1 to perform irreversible nucleotide edits. Inducible editing either deletes or inverts its target region, thus encoding information in three-state memory elements, or trits, and avoiding undesired recombination events. Using intMEMOIR combined with single-molecule fluorescence in situ hybridization, the authors were able to identify clonal structures as well as gene expression patterns in the fly brain, enabling both clonal analysis and expression profiling with intact spatial information. The ability to visualize cell lineage relationships directly within their native tissue context provides insights into development and disease. Science , this issue p. eabb3099

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 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 score0.076
Threshold uncertainty score0.243

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 head0.003
GPT teacher head0.234
Teacher spread0.231 · 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