MétaCan
Menu
Back to cohort
Record W4387167711 · doi:10.3103/s1052618823050060

Scientific Foundations for the Aerohydrodynamic Grinding of Lignocellulosic Raw Material under Steam Explosion

2023· article· en· W4387167711 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Machinery Manufacture and Reliability · 2023
Typearticle
Languageen
FieldEngineering
TopicCoal Combustion and Slurry Processing
Canadian institutionsnot available
Fundersnot available
KeywordsSteam explosionSuperheated steamPulverizerRaw materialDetonationSuperheatingNozzleWaste managementProcess engineeringEngineeringMaterials scienceNuclear engineeringEnvironmental scienceGrindingExplosive materialMechanical engineeringBoiler (water heating)Pulp and paper industryChemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract The results of studies aimed at improving the methods and equipment for the preliminary treatment of lignocellulosic raw materials by means of steam explosion are presented. The scientific basis for the outflow of superheated steam from a reactor in the course of detonation boiling of a liquid is described. Based on theoretical and experimental studies, it is shown that, in order to provide an increase in the efficiency of the steam explosion energy, it is appropriate to mount an aerohydrodynamic grinder (AHG) containing a Laval nozzle at the reactor outlet.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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