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Record W1590579934 · doi:10.1002/clen.201400590

Recycling of Paper Mill Biosolids: A Review on Current Practices and Emerging Biorefinery Initiatives

2014· review· en· W1590579934 on OpenAlexaff
Muhammad Pervaiz, Mohini Sain

Bibliographic record

VenueCLEAN - Soil Air Water · 2014
Typereview
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiorefineryBiosolidsIncinerationDispose patternBusinessWaste managementEnvironmental planningEngineeringNatural resource economicsEnvironmental scienceBiofuelEconomics

Abstract

fetched live from OpenAlex

In a rapidly urbanizing global society, the issue of solid waste management has become even more challenging in recent times. Manufacturing of paper, an integral part of human civilization, generates a substantial amount of effluent sludge, which invariably needs extra monetary resources to dispose‐off in millions of tons annually around the world. Currently, most of the widely practiced disposal options, landfilling and incineration, invariably pose serious environmental risks to immediate neighborhoods as well as to society at large. Recent stricter environmental legislations and general public awareness have been forcing paper producers to follow a sustainable approach in dealing with residual biomass generated at their facilities. At the same time, reducing waste at source and recycling have become an integral part of waste management of some responsible companies and governments of the industrialized world have also taken a number of initiatives in this regard. This review presents a holistic overview on current practices in dealing with paper sludge and their environmental and economic implications. Also presented is a comprehensive discussion on emerging biorefinery trends leading to value‐added utilization of primary, secondary and mixed biosolids originating from effluent treatments plants, especially from paper mill operations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.001

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 head0.049
GPT teacher head0.344
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations41
Published2014
Admission routes1
Has abstractyes

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