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Record W4413783656 · doi:10.54536/ajec.v4i3.3980

Environmental Impact of Poultry Waste Management Practices in Jhenaidah, Bangladesh

2025· article· en· W4413783656 on OpenAlexaff
Bristi Devnath, Anwar Hossain Rana, A.S.M. Mohiuddin, Partha Pratim Ghosh, Kazi Abdus Sobur, Md. Muraduzzaman, Shayed Mohaimen, Biplob Kumar Sarker

Bibliographic record

VenueAmerican Journal of Environment and Climate · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLivestock and Poultry Management
Canadian institutionsCentennial College
Fundersnot available
KeywordsBusinessEnvironmental planningPoultry farmingWaste managementEnvironmental scienceGeographyEngineering

Abstract

fetched live from OpenAlex

The poultry industry is one of the major agricultural sector which contributes to the economic development of Bangladesh. Improper poultry waste management practices causes environmental pollution and also public health hazards. Our study assessed the biosecurity measure and poultry waste management practices of 35 poultry farms in Jhenaidah Sadar Upazila, Bangladesh. Data were collected by the questionnaire from the poultry farmers. Our focusing on farmers demographics, farm characteristics, sanitation, and waste disposal practices. The results revealed that most farms were run by middle-aged, educated males. Around 77% of the owners lacked formal training on waste management. Poultry waste was predominantly disposed of on roadsides (62.85%) or in pits (37.14%). Waste was repurposed as manure (45.71%) or fish feed (22.85%) by some, but biosecurity measures remained inadequate. Complaints of noise and odour pollution were commonly reported by neighbours. Continued mismanagement led to practices of improper disposal, despite sporadic attempts at cleaning and disinfection through methods such as calcium oxide or potassium permanganate. Poultry waste can be utilized for composting or for biogas production to support environmental protection and public health.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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