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Record W4414366940 · doi:10.5376/ijms.2025.15.0018

Effect of Ocean Acidification on the Metabolism and Behavior of Tropical Sea Cucumbers

2025· article· en· W4414366940 on OpenAlexvenueno aff
Zhen Liu, Yeping Han

Bibliographic record

VenueInternational Journal of Marine Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsOcean acidificationCarbon dioxideSeawaterMetabolismTropicsEcosystemGlobal warming

Abstract

fetched live from OpenAlex

In recent years, CO₂ emitted by human activities has continued to rise. The ocean absorbs these CO₂ and has caused seawater acidification. It is expected that the pH of the sea surface will drop by 0.3~0.4 by the end of this century. Tropical sea cucumbers are the "engineers" of the subsea ecosystem, promoting organic degradation and nutrient circulation through feeding disturbances. This study reviews the effects of marine acidification on the metabolism and behavior of tropical sea cucumbers. Studies have shown that under low pH conditions, sea cucumbers have increased respiratory metabolic pressure, digestive enzyme activity is reduced, and more energy is used to maintain the acid-base balance in the body, and their growth and reproduction are limited. At the same time, sea cucumber feeding rate and defense behavior are inhibited, and habitat distribution may change. These changes will have a chain effect on tropical ecosystems such as coral reefs, weaken the nutrient circulation function, and affect ecological balance. In-depth research on the impact mechanism of marine acidification on sea cucumbers will help predict the response of marine ecosystems under climate change and provide scientific basis for resource conservation and aquaculture management.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.213
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.268
Teacher spread0.260 · 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.

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
Published2025
Admission routes1
Has abstractyes

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