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Record W4407912233 · doi:10.1201/9781003582311-17

Electronic Wastes, It's Impact on Maternal and Child Health

2025· book-chapter· en· W4407912233 on OpenAlexaff
Sangita Agarwal, Dibya Das, Sudipta Saha, Preeta Bose, Gourav Samajdar, Himangshu Sekhar Maji, Anupa Ruwali

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsSt Joseph's Health Centre
Fundersnot available
KeywordsEnvironmental healthChild healthInternet privacyMedicinePediatricsComputer science

Abstract

fetched live from OpenAlex

Electronic waste, sometimes known as "e-waste," is one of the most urgent environmental health hazards on a global scale due to its massive production volume and insufficient management strategy in many nations. In many developing and transitional economies, it presents a significant health danger for recyclers, scavenger employees, and residents in neighborhoods close to garbage disposal facilities. E-hazardous waste components are conjugated with a variety of harmful health effects. Waste CRT TVs, desktop computers, laptops, monitors, LCDs, cellular devices, keyboards, e-mouse, printers, and copiers are all various types of e-waste. E-waste includes metals and persistent organic pollutants (POPs), which are released into the environment due to improper recycling practices in several developing nations. Although these archaic recycling practices raise major health issues, the research demands must still be met. Numerous recognized and suspected developmental neurotoxicants in e-waste are especially dangerous to a growing fetus and kid. According to studies, children exposed to high amounts of lead had skin infections, respiratory problems, and stomach disorders.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.587
Threshold uncertainty score0.998

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)0.0030.000

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 head0.006
GPT teacher head0.246
Teacher spread0.240 · 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.

Study designNot applicable
Domainnot available
GenreOther

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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