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Record W4368367274 · doi:10.1080/17597269.2023.2206698

Thermo chemical conversion of cedar wood by pyrolysis technology for bio-oil

2023· article· en· W4368367274 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.

Bibliographic record

VenueBiofuels · 2023
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsMcGill University
Fundersnot available
KeywordsPyrolysisCharBiomass (ecology)BiofuelParticle sizeChemistryPyrolysis oilChemical engineeringPulp and paper industryMaterials scienceWaste managementOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The conversion of cedar wood which is abundantly found in the forests of Japan, into biofuels and chemicals by externally heated fixed-bed pyrolysis reactor has been taken into consideration in this study. The selected solid biomass in particle form were fed into the reactor by gravity feed type reactor feeder. The output products were liquid (oil), solid char, and gas. The liquid and char products were collected separately while the gas was flared into the atmosphere. The process conditions were found to influence the product yields significantly. The maximum liquid yields were 48 wt% of solid particles at reactor temperature 450 °C for N2 gas flow rate 6 L/min, feed particle size 1180–1700 µm, and running time 30 min. The liquid product obtained at this optimum condition was characterized by physical properties, chemical analysis, and gas chromatograph mass spectrometry techniques. The results show that it is possible to obtain liquid product from cedar wood that are comparable to petroleum fuels and other valuable chemicals.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.564

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 science0.0000.000
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 head0.008
GPT teacher head0.209
Teacher spread0.202 · 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