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Record W4293007651 · doi:10.11159/icbb22.022

Fractionating of synthetic biology waste stream into nutrient rich extract for cosmetic industry

2022· article· en· W4293007651 on OpenAlexvenueno aff
Bruno Horta, Carla Cálix, João da Silva Burgal, Catarina S. Oliveira

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsnot available
Fundersnot available
KeywordsNutrientWaste streamWaste managementEnvironmental sciencePulp and paper industryChemistryBiochemical engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Amyris is a world-leading manufacturer of sustainable ingredients producing environmentally friendly products. Biofene (or trans--farnesene) is a synthetic biology derived product using yeast fermentation of sugarcane. This production originates spent broth, an aqueous waste stream typically applied to land by fertirrigation. On one hand, spent broth contains recoverable added-value compounds; on the other hand, an improper disposal of this waste stream could result in negative effects and give rise to environmental concerns. Hence the importance of implementing the bio-waste refinery and circular bio-economy concepts for recovering high-value bio-products. Cosmetic industries are committed to find natural, sustainable, and functional ingredients meeting consumer's and entities rigorous demands. Spent broth may be a source of such ingredients. Thus, the objective of this work was to assess the fractionation of the spent broth stream from Biofene production to obtain a concentrated nutrient rich extract with potential application on cosmetics.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.015
GPT teacher head0.243
Teacher spread0.227 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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