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Record W3018733063 · doi:10.1002/bit.27364

The importance and future of biochemical engineering

2020· article· en· W3018733063 on OpenAlexafffund
Timothy A. Whitehead, Scott Banta, William E. Bentley, Michael J. Betenbaugh, Christina Chan, Douglas S. Clark, Corinne A. Hoesli, Michael C. Jewett, Beth Junker, Mattheos Koffas, Rashmi Kshirsagar, Amanda Lewis, Chien‐Ting Li, Costas D. Maranas, Eleftherios T. Papoutsakis, Kristala L. J. Prather, Steffen Schaffer, Laura Segatori, Ian Wheeldon

Bibliographic record

VenueBiotechnology and Bioengineering · 2020
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsMcGill University
FundersArmy Research LaboratoryArmy Research OfficeCenter for Bioenergy InnovationDivision of Chemical, Bioengineering, Environmental, and Transport SystemsDivision of Molecular and Cellular BiosciencesNational Institute of Allergy and Infectious DiseasesMultidisciplinary University Research InitiativeMcGill UniversityU.S. Department of EnergyNational Institutes of HealthNational Science Foundation
KeywordsBiochemical engineeringChemistryComputational biologyBiotechnologyEngineeringBiology

Abstract

fetched live from OpenAlex

Today's Biochemical Engineer may contribute to advances in a wide range of technical areas. The recent Biochemical and Molecular Engineering XXI conference focused on "The Next Generation of Biochemical and Molecular Engineering: The role of emerging technologies in tomorrow's products and processes". On the basis of topical discussions at this conference, this perspective synthesizes one vision on where investment in research areas is needed for biotechnology to continue contributing to some of the world's grand challenges.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.013
Scholarly communication0.0060.011
Open science0.0010.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.004
GPT teacher head0.166
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations16
Published2020
Admission routes2
Has abstractyes

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