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Record W2411113113 · doi:10.1385/1-59259-192-2:413

Computing with DNA

2003· article· en· W2411113113 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

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Biological Computing
Canadian institutionsWestern University
Fundersnot available
KeywordsComputationComputer scienceElectronicsEngineeringElectrical engineeringAlgorithm

Abstract

fetched live from OpenAlex

A brief look at the history of humanity shows that since the earliest days people needed to count and compute, either for measuring the months and the seasons or for commerce and construction. The means used for performing calculations were whatever was available, and thus progressed gradually from manual (digits) to mechanical (abacus, mechanical adding engine), and from there on to electronic devices. Electronic computers are only the latest in a long chain of human efforts to use the best technology available for performing computations. Although it is true that their appearance, some 50 years ago, has revolutionized computing, electronic computers mark neither the beginning nor the end of the history of computation. Indeed, even electronic computers have their limitations: There is a limit to the amount of data they can store, and physical laws dictate the speed thresholds they will soon reach. The most recent attempt to break down these barriers is to replace, once more, the tools for performing computations with biological ones instead of electrical ones.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.335

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 head0.035
GPT teacher head0.252
Teacher spread0.217 · 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