Mapping of marine lobster research: A global outlook
Bibliographic record
Abstract
This study examined research and development of the commercially important marine lobster, in terms of descriptive metadata and scientometric analysis. We consider a collection of over 9,578 related articles to assess literature patterns and research development trends related to marine lobsters as a topic. Web of Science Core Collection was used to identify, collate, and generate publication trends, a list prominent authors and affiliations involved, countries that actively participated, and the co-citation analysis of the references as well as impactful articles and keywords. There were 149 different countries or states that had relevant publications on lobster research. We found an increase in the number of publications over time, with the USA having the most number of publications, followed by Australia and Canada. A total of 17,782 authors were involved in the field of lobster studies. Canadian researchers had the highest citation count for marine lobster research. Surprisingly, the most impactful keyword was crayfish, followed by neuron and amino acid sequence. Our study identifies the multidisciplinary nature in marine lobster research, which includes fields such as neuroscience and developmental genetics. We find that lobster scientific publications increasingly center on the broader coverage areas of science such as taxonomy and basic biology.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.065 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".