MétaCan
Menu
Back to cohort
Record W2936986376 · doi:10.1016/j.dib.2019.103913

A comprehensive dataset on anaerobic digestion of cattle manure, source separated organics, and municipal sludge using different inoculum sources

2019· article· en· W2936986376 on OpenAlexafffund
E. Hosseini Koupaie, A. Azizi, Amir Abbas Bazyar Lakeh, Hisham Hafez, Elsayed Elbeshbishy

Bibliographic record

VenueData in Brief · 2019
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsGreenField Specialty Alcolhols (Canada)Toronto Metropolitan University
FundersOntario Water Consortium
KeywordsDigestateAnaerobic digestionBiogasMesophileMethaneManureEnvironmental sciencePulp and paper industryWaste managementBiodegradationBioenergyChemistryBiofuelEnvironmental chemistryAgronomyBiologyEngineering

Abstract

fetched live from OpenAlex

The dataset reported in this article provides quantitative data on anaerobic digestion of cattle manure, source separated organics (SSO), primary sludge (PS), and thickened waste activated sludge (TWAS) using different inoculum sources. The discussion and interpretation of the data are provided in another publication entitled "Comparison of liquid and dewatered digestate as inoculum for anaerobic digestion of organic solid wastes" [1]. The data presented in this article include 1) the gas chromatography (GC) procedure of determining the biogas composition, 2) the procedure of converting the daily biogas/methane production data from the experimental condition (mesophilic temperature of 38 °C and room pressure) to the standard temperature (0 °C) and pressure (1 atm) condition, 3) the specific methanogenic activity data, and 4) the methane daily production rate data, and 5) the organics biodegradation kinetic rates.

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)0.001
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.031
GPT teacher head0.260
Teacher spread0.229 · 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
GenreDataset

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

Citations2
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueData in BriefSame topicAnaerobic Digestion and Biogas ProductionFrench-language works237,207