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Record W2563102490 · doi:10.5558/tfc2016-080

An approach for the use of agricultural by-products through a biorefinery in Bangladesh

2016· article· en· W2563102490 on OpenAlexvenueno aff
M. Sarwar Jahan, Mohammad Nashir Uddin, A.F.M. Akhtaruzzaman

Bibliographic record

VenueThe Forestry Chronicle · 2016
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
Fundersnot available
KeywordsBiorefineryBiofuelAgricultureBusinessProduct (mathematics)Production (economics)Renewable resourceNatural resource economicsRenewable energyBiotechnologyEnvironmental scienceWaste managementEngineeringGeographyEconomicsMathematics

Abstract

fetched live from OpenAlex

The global need for developing renewable, sustainable, biomaterials, biochemicals and biofuels continues to grow along with increasing worldwide desire to reduce fossil-fuel emissions. An appealing source for bio-based products is lignocellulosic resources, which are abundant, low cost, and are often a by-product of food production (mainly rice). This paper gives an approach for bio-based product development in Bangladesh by analyzing i) a comprehensive inventory of agricultural and lignocellulosic wastes, ii) the characteristics of these wastes, and iii) suitable methods for producing bio-based products. It is proposed that a cooperative society be set up amongst the rice producing farmers and communities. Entrepreneurs would collaborate with this cooperative society to implement the approach, and biorefinery plants could be established in different parts of the country based on the amount of available agricultural wastes in specific areas. As forest area is very limited and population density is very high in Bangladesh, wood resources cannot be utilized in biofuel, biochemicals and biomaterials production in the country, making agricultural by-products the only real option available.

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 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 score0.073
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.221
Teacher spread0.188 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2016
Admission routes1
Has abstractyes

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