The Evolution of Coral Reef under Changing Climate: A Scientometric Review
Bibliographic record
Abstract
In this scientometric review, we employ the Web of Science Core Collection to assess current publications and research trends regarding coral reefs in relation to climate change. Thirty-seven keywords for climate change and seven keywords for coral reefs were used in the analysis of 7743 articles on coral reefs and climate change. The field entered an accelerated uptrend phase in 2016, and it is anticipated that this phase will last for the next 5 to 10 years of research publication and citation. The United States and Australia have produced the greatest number of publications in this field. A cluster (i.e., focused issue) analysis showed that coral bleaching dominated the literature from 2000 to 2010, ocean acidification from 2010 to 2020, and sea-level rise, as well as the central Red Sea (Africa/Asia), in 2021. Three different types of keywords appear in the analysis based on which are the (i) most recent (2021), (ii) most influential (highly cited), and (iii) mostly used (frequently used keywords in the article) in the field. The Great Barrier Reef, which is found in the waters of Australia, is thought to be the subject of current coral reef and climate change research. Interestingly, climate-induced temperature changes in "ocean warming" and "sea surface temperature" are the most recent significant and dominant keywords in the coral reef and climate change area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.092 | 0.149 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".