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Record W4390036664 · doi:10.26434/chemrxiv-2023-vff9v

Organosilicon Biotechnology II: Biocatalysis at Silicon

2023· preprint· en· W4390036664 on OpenAlexaff
Mark B. Frampton

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsLoyalist College
Fundersnot available
KeywordsOrganosiliconSiliconBiocatalysisChemistryNanotechnologyBiochemical engineeringOrganic chemistryMaterials scienceEngineeringCatalysisReaction mechanism

Abstract

fetched live from OpenAlex

The application of biochemical methods in organosilicon chemistry has the potential to provide access to otherwise unattainable chemical structures. The development of biochemical methods in silicon chemistry has been under exploration for approximately 40 years, and recently, methods have been developed for catalyzing transformations at silicon in which proteins have been shown to catalyze new-to-nature transformations. These new methods complement previous work in which enzymes performed more traditional transformations in which silicon generally played the role of spectator. This overview will cover recent developments in the field of organosilicon biotechnology focusing on the use of peptides and proteins to mediate transformations at silicon.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.008

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 head0.036
GPT teacher head0.292
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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