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Record W3183586790 · doi:10.32672/jse.v6i3.3120

Pengolahan Sampah Organik dan Limbah Biomassa dengan Teknologi Olah Sampah di Sumbernya

2021· article· en· W3183586790 on OpenAlexaff
I. Made Brunner, Arief Norhidayat, Satria M. Brunner

Bibliographic record

VenueJurnal Serambi Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWaste Management and Recycling
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPelletizingPelletsBiomass (ecology)Pulp and paper industryWaste managementcardboardWater contentPelletEnvironmental scienceMunicipal solid wasteTorrefactionBiodegradable wasteMoistureRaw materialChemistryMaterials sciencePyrolysisAgronomyEngineeringComposite material

Abstract

fetched live from OpenAlex

Solid Waste Processing Technology at the Source (TOSS) is an alternative method in processing organic and biomass waste on a communal scale. The processing of organic and biomass waste material is carried out in three stages of the process: biodrying which utilizes the aerobic activity of microorganisms; chopping which is intended to refine the material, and pelletization to compact the material into biomass pellets. The biodrying process in bamboo boxes is able to reduce the water content in organic and biomass waste within 4-5 days. Sorting of non-organic materials can be done at an early stage before or after the biodrying process. Sorting is required before chopping and pelletizing process to avoid unnecessary machine break down. The biomass pellet which is the final product has a diameter of about 10 mm with a length between 10-40 mm, a calorific value between 3000-4000 kcal/kg, and a moisture content of up to 15%. Compared to coal, biomass pellets tend to have higher volatile and ash content, while lower ash and sulfur content.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.003

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations10
Published2021
Admission routes1
Has abstractyes

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