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Record W216834423 · doi:10.1021/jf502556t

Mapping Thermomechanical Pulp Sludge (TMPS) Biochar Characteristics for Greenhouse Produce Safety

2015· article· en· W216834423 on OpenAlexafffund
Ataullah Khan, Mohyuddin Mirza, Brian Fahlman, Ryan Rybchuk, Jian Yang, Don Harfield, Anthony O. Anyia

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2015
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsBC Greenhouse Growers AssociationAlberta Innovates
FundersAgriculture and Agri-Food CanadaAlberta Innovates - Technology Futures
KeywordsBiocharPyrolysisPulp (tooth)Pulp and paper industryRaw materialGreenhouseChemistryCharcoalEnvironmental scienceEnvironmental chemistryWaste managementAgronomyOrganic chemistry

Abstract

fetched live from OpenAlex

This study evaluates the existence of toxic compounds in thermomechanical pulp sludge (TMPS) derived biochars obtained through a slow pyrolysis process and establishes the criteria for manufacturing benign-quality biochar for safe greenhouse-based food production. Accordingly, nine TMPS biochars generated at different temperatures (450, 500, 550 °C) and residence times (30, 60, 120 min) were investigated. Depending on the production conditions, the polycyclic aromatic hydrocarbons (PAHs) sum varied from 0.4 to 236 μg/g biochar. Interestingly, correlations between the PAH content, toxicity, and process conditions were derived in the form of process toxicity relationships (PTRs). On the basis of the learning garnered in this study, it is recommended that TMPS feedstock will yield benign quality biochar when processed at a minimum 500 °C temperature for an optimum residence time of 30 min.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.015
GPT teacher head0.191
Teacher spread0.176 · 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 designObservational
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

Citations14
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Agricultural and Food Chemistry→Same topicThermochemical Biomass Conversion Processes→French-language works237,207→