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Record W4399767734 · doi:10.54097/98qm7j97

Impact of Climate Change on Marine Coral Reef Communities

2024· article· en· W4399767734 on OpenAlexaff
Yubing Lin

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCoral reefCoral bleachingOcean acidificationReefClimate changeResilience of coral reefsEffects of global warming on oceansEnvironmental scienceGlobal warmingOceanographyMarine ecosystemEnvironmental issues with coral reefsCoralEffects of global warmingEcosystemCoral reef organizationsGlobal changeCoral reef protectionEcologyGeologyBiology

Abstract

fetched live from OpenAlex

In recent years, the phenomenon of climate change has intensified globally, which includes but is not limited to, global warming, extreme weather events, and changes in climate patterns. This has raised concerns among environmental organizations about the future of the global ecosystem. This paper focuses on the effects of climate change on marine coral reef communities and based on experimental data, literature information, and existing datasets to develop mathematical models to analyze the effects of global warming, ocean acidification, sea level rise, and ocean storms on coral bleaching, reef calcification, and other issues. The scientists documented the changes in coral reef status through regular monitoring and conducted laboratory studies to simulate the growth, bleaching, and survival of coral reefs under different climatic conditions, such as changes in water temperature and Pondus Hydrogenii (PH). Satellite imagery is used to understand the global distribution of coral reefs, their coverage, and changes in biodiversity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.012
GPT teacher head0.238
Teacher spread0.227 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2024
Admission routes1
Has abstractyes

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