Global Research Climate Change and Whale Domain: A Scientometric Review
Bibliographic record
Abstract
This study analyzed the research and development landscape in the domain of climate change and whale studies through descriptive metadata and scientometric analysis. Using the Web of Science Core Collection (WoS), we compiled a total of 824 relevant articles and analyzed publication trends, authors and affiliations, countries involved, references, impactful articles, and significant keywords. One notable finding was the involvement of 74 different countries or states with relevant publications in the field of climate change and whale studies, with the USA leading in terms of publication count, followed by Canada and Australia. A total of 3,229 active authors in whale studies were identified, with Canadian researchers having the highest citation count in climate change and whale studies. Surprisingly, the most impactful keywords identified were “climate change” and “marine mammals.” Additionally, the emergence of new keyword burst trends such as “marine” and “management” highlights the need for increased funding or the application of innovative methodologies to encourage more scientifically original research articles in the field of climate change and whale studies. These growing trends emphasize the ongoing importance of sustained inquiry and exploration to effectively address the challenges and opportunities in this evolving field.
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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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.104 | 0.130 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".