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Record W4226006894 · doi:10.3934/environsci.2022008

Impact of COVID-19 on the environment sector: a case study of Central Visayas, Philippines

2022· article· en· W4226006894 on OpenAlex
Clare Maristela V. Galon, James G. Esguerra

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

VenueAIMS environmental science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)GeographyEnvironmental qualityGovernment (linguistics)Environmental planningEnvironmental protectionPollutionSocioeconomicsBusinessEnvironmental resource managementEnvironmental sciencePolitical scienceEcologyEconomics

Abstract

fetched live from OpenAlex

<abstract> <p>The pandemic has underscored the importance of the environment. In this study, the environmental condition of Central Visayas, Philippines has been assessed and evaluated before and during the onset of the COVID-19 pandemic to deal with a possible association between the environmental indicators and the pandemic. The relationships between environmental key variables namely: air quality, air pollution, water quality, water pollution, and solid waste management have been quantified. The study utilized secondary data sources from a review of records from government agencies and LGUs in Region 7. This study also provides a framework which is the pandemics and epidemics in environmental aspects. The paper concludes by offering researchers and policymakers to promote changes in environmental policies and provide some recommendations for adequately controlling future pandemic and epidemic threats in Central Visayas, Philippines.</p> </abstract>

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0280.000

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 head0.040
GPT teacher head0.319
Teacher spread0.279 · 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