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Waste Management Under the Legal Framework in India

2019· book-chapter· en· W2980122101 on OpenAlexaboutno aff
Sadhan Kumar Ghosh

Bibliographic record

VenueAdvances in environmental engineering and green technologies book series · 2019
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaGeographyQuarter (Canadian coin)CensusPopulationEnvironmental planningReuseEconomic growthSocioeconomicsEngineeringArchaeologyWaste managementEconomicsSociology

Abstract

fetched live from OpenAlex

India, the second biggest country in the world, has nearly 1.25 million people living in 29 states and seven union territories covering an area of 3,287,000 sq. km. India's economy grew at an impressive 8.2% in the first quarter of 2018-19. Traditionally, India has the habit of reuse and recycling the materials wherever possible. As the city agglomeration is increasing the waste generation is increasing. The number of towns/cities have increased from 5,161 in 2001 to 7,935 in 2011, whereas the number of metropolitan cities having million plus population has increased from 35 to 53 number as per 2011 census. It is projected that half of India's population will live in cities by 2050. Waste management in India has been experiencing a paradigm shift through the establishment of Swachh Bharat Mission in urban and rural India in 2014 and the revision and establishment of waste management rules in six types of wastes including transboundary movement in 2016. This study presents the overall waste management scenario and the legal framework in India.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.002
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.003
GPT teacher head0.178
Teacher spread0.175 · 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 designQualitative
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

Citations6
Published2019
Admission routes1
Has abstractyes

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