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Record W4392969756 · doi:10.5376/ijms.2024.14.0003

The Physiological and Ecological Effects between Ocean Acidification and Coral Reefs

2024· article· en· W4392969756 on OpenAlexvenueno aff
Jinni Wu, Xiaoying Xu, WU Li-mei

Bibliographic record

VenueInternational Journal of Marine Science · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsOcean acidificationCoral reefResilience of coral reefsCoralEnvironmental scienceEnvironmental issues with coral reefsEcologyReefOceanographyBiologyClimate changeGeology

Abstract

fetched live from OpenAlex

In recent decades, the concentration of CO 2 in the atmosphere has continued to rise, leading to global changes such as global warming and ocean acidification. The CO 2 in the atmosphere is dissolved in the ocean through water gas exchange to achieve water gas balance. The continuously increasing CO 2 concentration changes the marine hydrochemical system, especially breaking the original carbonate equilibrium system, reducing the pH value, carbonate ion concentration, and calcium carbonate saturation in seawater, leading to ocean acidification. A normal seawater hydrochloric acid system can promote the biological activity of coral reef ecosystems, while ocean acidification not only leads to a decrease in the calcification rate of calcified organisms in coral reef systems, but also leads to dissolution phenomena in coral reef systems. This review analyzes the phenomenon of ocean acidification, understands the ecological effects of coral reef systems in the process of ocean acidification, proposes corresponding protection measures, and hopes to strengthen the protection of the ocean and coral reef systems globally.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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