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Record W4403176507 · doi:10.70436/nuijb.v3i02.277

Ozone Layer Depletion: Causes, Effects and Prevention

2024· article· en· W4403176507 on OpenAlexaboutno aff
Attaullah Mirzakhil, Gul Asghar Aluzai, Khodaidad Khochai, Fazalkarim Elmi, Saifullah Shinwari

Bibliographic record

VenueNangarhar University international journal of biosciences. · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsOzone depletionLayer (electronics)OzoneOzone layerEnvironmental scienceMeteorologyMaterials scienceNanotechnologyGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to review the Causes, effects and prevention of the ozone layer depletion. Ozone layer is a part of the stratosphere which absorbs the harmful ultraviolet (UV) rays of the sun and prevents it from reaching the earth surface. Industrial activities, release chemicals into the air that can destroy ozone layer, this process known as ozone layer depletion. The main causes of ozone layer depletion are Chlorofluorocarbons (CFCs), Halons (HCFCs) and NOX (nitrogen oxides), depletion of stratospheric ozone, led to increased ultraviolet radiation at the earth’s surface as well as spectral shifts to the more biologically damaging shorter wavelengths, which is harmful to the human, plants and ecosystem. An international environmental initiative to lessen ozone depletion is known as the Montreal Protocol. This agreement required all nations to impose legal obligations on the reduction of CFCs and other comparable chemical compounds. As a result, an international fund has been set up to assist in introducing these nations to new and environmentally friendly technologies and chemicals.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueNangarhar University international journal of biosciences.→Same topicAir Quality and Health Impacts→French-language works237,207→