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Record W4286255946 · doi:10.1016/j.crgsc.2022.100330

Blue is the new green: Valorization of crustacean waste

2022· article· en· W4286255946 on OpenAlexafffund
Juliana L. Vidal, Tony Jin, Edmond Lam, Francesca M. Kerton, Audrey Moores

Bibliographic record

VenueCurrent Research in Green and Sustainable Chemistry · 2022
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsNational Research Council CanadaMemorial University of NewfoundlandMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds de recherche du Québec – Nature et technologiesNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaFonds de recherche du QuébecCentre in Green Chemistry and CatalysisMcGill University
KeywordsBiorefineryBiomass (ecology)Environmental scienceRenewable energyWaste managementBiofuelEcologyEngineeringBiology

Abstract

fetched live from OpenAlex

Every year, large amounts of marine biomass waste are generated around the globe. In the case of crustaceans, an opportunity is loss to convert these chemically rich streams into important and industrially relevant materials (e.g., chitin, chitosan, calcium carbonate, proteins, and other nitrogen-containing compounds) as these residues are often landfilled or directly discarded in the environment. Current processes to produce chemicals from marine biomass rely on wasteful, chemically- and energy-intensive methods that can harm human health and the environment and produce materials with limited applicability to downstream applications. Herein, an overview of the current status of marine biomass valorization is presented, as well as a comparison between traditional and more sustainable methods for the extraction of chemicals from waste crustacean shells. The pathways for the synthesis of nanomaterials from marine biomass is also highlighted, alongside with the synergic correlation between a ‘greener’ strategy for the implementation of a marine biorefinery and the United Nations Sustainable Development Goals.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.056
GPT teacher head0.311
Teacher spread0.255 · 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.

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

Citations54
Published2022
Admission routes2
Has abstractyes

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