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Record W4393165606 · doi:10.1016/j.tifs.2024.104426

Bacterial single cell protein (BSCP): A sustainable protein source from methylobacterium species

2024· article· en· W4393165606 on OpenAlexafffund
Marttin Paulraj Gundupalli, Sara Ansari, Jaquelinne Pires Vital da Costa, Feng Qiu, Jay A. Anderson, Marty Luckert, David C. Bressler

Bibliographic record

VenueTrends in Food Science & Technology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsMethylobacteriumSingle-cell proteinFlexibility (engineering)BusinessBiotechnologyProduction (economics)Biochemical engineeringBiologyFood scienceBacteriaFermentationEngineeringEconomicsGeneticsMicroeconomics

Abstract

fetched live from OpenAlex

Single Cell Protein (SCP) is a potential replacement for traditional protein sources in both food and feed. This review specifically focuses on SCP produced by bacteria, emphasizing its unique advantages such as rapid growth, high protein content, efficient manufacturing, genetic flexibility, and thorough strain characterization. Notably, among other microorganisms, Methylobacterium species have recently been found to be crucial for SCP synthesis. The review examines the feasibility of incorporating SCP as a sustainable substitute in protein diets for aquaculture and cattle production by closely analyzing fishmeal and soybean meal. Additionally, it highlights the importance of ongoing research and supportive legislation, while also considering economic and policy factors to assess the financial viability of SCP production. The review concludes by outlining future prospects and the outlook for SCP derived from Methylobacterium species, providing a forward-looking perspective on the evolving applications of SCP.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.271
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; 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 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

Citations41
Published2024
Admission routes2
Has abstractyes

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