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Record W3082121099 · doi:10.5539/mas.v14n9p63

The Potentiality of Textile Sludge to be Used as Organic Manure

2020· article· en· W3082121099 on OpenAlexvenueno aff
Tarek Hossain Raju, Shakil Mahmud, Belayet Hossain, Masrur Sabir Nafee, Sayeda Ariana Ferdous, Md Mohsin Patwary, Mohammad Nazmul Hossen, Shahadat Hossain

Bibliographic record

VenueModern Applied Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsOrganic matterEffluentCompostManurePulp and paper industryEnvironmental scienceTextileNutrientHeavy metalsTextile industryEnvironmental chemistryChemistryWaste managementAgronomyEnvironmental engineeringMaterials scienceBiology

Abstract

fetched live from OpenAlex

Textile Industries of Bangladesh confront the environment a huge threat. It produces a huge amount of sludge from effluent treatment plants that is a burden for industries for its high volume and proper treatment cost thereby a huge threat to the environment. The present study aims to characterize the solid sludge of textile industry in terms of pH, organic matter (OM), nutrient elements (N, P, K) and other metal elements through proper analytical methods. The average pH value of the sludge sample was 8.28 along with moisture content 60.64%. The average content of the OM of the studied sludge samples was 11.73% and the average values of N, P and K were 7.57%, 0.52% and 0.50%, respectively. The studied metal (Cr, Zn Mn, Cd, Pb, As and Cu) content of the sludge revealed that the toxic heavy metals Pb, Cd and As were not found in the samples. This study reveals that the amount of OM, N, P and pH is within the Waste concern compost standard (WCCS). The findings clearly show a potentiality of textile sludge as organic manure as they are rich in OM and plant nutrients and free from toxic heavy metals as well.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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