MétaCan
Menu
Back to cohort
Record W4232792048 · doi:10.1515/iupac.66.0001

Biochemical Engineering in Biotechnology

2016· dataset· en· W4232792048 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

VenueIUPAC Standards Online · 2016
Typedataset
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBioprocessBiotechnologyBioprocess engineeringBiochemical engineeringIndustrial biotechnologyEngineeringProcess (computing)Metabolic engineeringChemistryComputer scienceBiology

Abstract

fetched live from OpenAlex

The role of biochemical engineering in biotechnology, as the technology of industrial exploitation of biochemical systems, is illustrated. The scientific disciplines underlying biotechnology are pointed out. Biochemical engineering is described as an established discipline, drawing upon chemical, biological and engineering sciences and concerned with design, development, implementation and operation of processes and process plant for handling biological materials and biocatalysts. The principal aspects of bioprocessing are outlined, including processing steps, biocatalysts, bioreactors and downstream processing considerations, to impart a flavour of the biochemical engineering activities and their interaction with chemical sciences. As shown by examples, applications of bioprocessing and biochemical engineering are wide ranging, covering such major facets of civilization as healthcare, agriculture and food, resource recovery, bulk and fine chemicals, energy and environmental pollution abatement.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.005
Threshold uncertainty score1.000

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.297
Teacher spread0.291 · 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