MétaCan
Menu
Back to cohort
Record W2897468436 · doi:10.1002/9781119248002.ch7

Microbial Metabolic Pathways in the Production of Valued‐added Products

2018· other· en· W2897468436 on OpenAlexaff
Gilberto Vinícius de Melo Pereira, Ana Maria de Oliveira Finco, Luiz Alberto Júnior Letti, Susan Grace Karp, Maria Giovana Binder Pagnoncelli, Juliana de Oliveira, Vanete Thomaz‐Soccol, Satinder Kaur Brar, Carlos Ricardo Soccol

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBiofuelBiohydrogenFossil fuelBiogasBiomass (ecology)Renewable energyBiochemical engineeringMetabolic engineeringBiotechnologyChemistryEnvironmental scienceWaste managementBiologyOrganic chemistryEngineeringEnzymeEcology

Abstract

fetched live from OpenAlex

This chapter explores the major metabolic pathways, nutritional requirements and alternative carbon sources in the production of industrially important by-products. Microbial cells possess thousands of different molecules which are responsible for forming a defined molecular structure. The increase in the number of cells is known as microbial growth. Among the thousands of microbial compounds, products derived from biomass are one such emerging class of molecules. It is known that the production of enzymes can be influenced by many environmental conditions. Fossil fuels are the major energy source used in the world today. Biofuels, such as biodiesel, biohydrogen, bioethanol, biobutanol, biogas and biomethanol, are sustainable alternatives to meet the world energy demand. Organic acids serve as precursors for a variety of bulk chemicals and commercially important polymers. Rare sugars, referred to as monosaccharides and their derivatives that rarely exist in nature, have various applications in the food industry, agricultural chemicals and pharmaceuticals.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.401
Threshold uncertainty score0.690

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 head0.010
GPT teacher head0.211
Teacher spread0.202 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreOther

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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicMicrobial Metabolic Engineering and BioproductionFrench-language works237,207