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A Survey on Various Aproaches to e-waste management

2022· article· en· W4221082560 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 International Conference on Computer Communication and Informatics (ICCCI) · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsElectronic wasteReuseElectronicsSustainabilityElectronic equipmentWaste managementControl (management)BusinessSolid waste managementCleaner productionMunicipal solid wasteEngineeringComputer science

Abstract

fetched live from OpenAlex

This paper examines people's awareness of e-waste management, its generation, and main treatment techniques in educational institutions with the goal of revealing the role of e-waste management practices for environmental sustainability. The empirical research is being carried out in India using a well-structured questionnaire that was sent to engineering students. Generation, management, type of electronic waste components, progressive usage of electronic waste management, control, and training are six essential areas of electronic waste that are investigated. Electronic waste, often known as e-waste, is a term used to describe electronics that have been discharged electrically or electronically. Used electronic equipment destined for refurbishment, reuse, resale, recycling through material recovery, or destruction are classified as electronic waste.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.272
Teacher spread0.223 · 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