MétaCan
Menu
Back to cohort
Record W2197420772 · doi:10.1021/acs.iecr.5b02603

Supercritical Water Gasification of Lactose as a Model Compound for Valorization of Dairy Industry Effluents

2015· article· en· W2197420772 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldEngineering
TopicSubcritical and Supercritical Water Processes
Canadian institutionsMcGill UniversityYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLactoseSupercritical fluidChemistryEffluentResidence time (fluid dynamics)Pulp and paper industryWaste managementFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The dairy industry effluents, including whey waste and milk-based residues, are enriched in lactose and minor amounts of glucose that could potentially be converted to biofuels and biochemicals. Lactose was used in this work as a model compound of dairy effluents for gasification in supercritical water using a continuous flow tubular reactor. Four parameters impacting supercritical water gasification were studied, namely, temperature (550–700 °C), residence time (30–75 s), feed concentration (4–10 wt %), and catalyst concentration (0.2–0.8 wt %). The best total gas yields, carbon gasification efficiency, H 2 yields, and other major gases (CO 2 and CH 4 ) were obtained at 700 °C using a feed concentration of 4 wt % lactose and a residence time of 60 s at fixed pressure of 25 MPa. Furthermore, catalytic lactose gasification involving 0.8 wt % Na 2 CO 3 resulted in maximum H 2 yield (22.4 mol/mol) compared to those obtained by 0.8 wt % K 2 CO 3 (21.5 mol/mol) and noncatalytic gasification (16 mol/mol).

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.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0010.001
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 head0.188
GPT teacher head0.357
Teacher spread0.169 · 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