MétaCan
Menu
Back to cohort
Record W4405857436 · doi:10.22214/ijraset.2024.66093

Intelligent Waste Management Classification

2024· article· en· W4405857436 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

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsScalabilitySustainabilityUploadBusinessCircular economyArchitectureComputer scienceEnvironmental economicsWaste managementEngineeringDatabaseWorld Wide WebEconomics

Abstract

fetched live from OpenAlex

Addressing the global waste management crisis requires innovative approaches that integrate technology and sustainability. This research introduces an e-commerce platform designed to facilitate the recycling ecosystem by connecting waste sellers, such as individuals and industries, with buyers seeking recyclable materials. The platform utilizes machine learning-based image classification to analyze uploaded waste images, accurately identify their types, and link sellers to appropriate buyers. Furthermore, it extends its functionality by allowing sellers to list and sell recycled products, thereby completing the recycling loop. By streamlining the exchange of waste and recycled goods, this system fosters a circular economy, promotes sustainable practices, and provides a scalable solution to reduce environmental impact. The paper details the platform’s architecture, technical implementation, and its potential to revolutionize waste management practices.

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)0.003
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.069
GPT teacher head0.384
Teacher spread0.315 · 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