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Kosten en baten van innovatie afwegen Webtool helpt bij maken business case

2012· article· en· W38232680 on OpenAlexfundno aff
C. de Feijter

Bibliographic record

VenueFood Chemistry · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

The interest in shrimp shell valorization has been growing in line with sustainability goals. Therefore, the main objective of this study was to obtain chitosan from shrimp shell using ultrasound followed by subcritical water treatment. Ultrasonication of shells was performed at 600 and 1200 W for 5 min. Then, shells were hydrolyzed at 140-260 °C and 50 bar for 10-60 min followed by demineralization using citric acid, bleaching using hydrogen peroxide and deacetylation using sodium hydroxide solution. The highest deproteination (80.93 %) was obtained by ultrasonication at 1200 W/5 min followed by subcritical water hydrolysis at 260 °C/50 bar/60 min, where the residue with a yield of 10.56 %, whiteness index of 60.42, degree of deacetylation of 64.27 %, relative crystallinity of 32.66 % and similar functional groups to the commercial sample was obtained. These results indicated that the combination of ultrasound with subcritical water is promising to valorize shrimp shell towards production of value-added compounds.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.000
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 head0.205
Teacher spread0.189 · 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 designNot applicable
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
Published2012
Admission routes1
Has abstractyes

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