An overview of global research effort in fisheries science
Bibliographic record
Abstract
Abstract We used bibliometric indicators to characterize recent (2010–2013) research activity in fisheries science with the objective of garnering insights into how this increased effort has been directed. Specifically, we provide an overview of the primary literature on fisheries research, including which countries are the largest contributors (USA, China, Japan, Australia, Canada, and Norway), and an assessment of the citation impact of the research conducted by different countries. The countries with the highest impact were the UK, Norway, Germany, France, Canada, and Italy. We further assessed the research topics that are most commonly studied and attempt to understand what drives that. During the past three decades, research appears to have shifted from a focus on species-related questions to processes. An analysis of how publication output is distributed at the level of fish species indicates that a small number of species (e.g. Atlantic salmon, rainbow trout, and Atlantic cod) account for a disproportionate volume of the total research effort. Interestingly, publication output is not correlated with the commercial importance of a species. Although fisheries management is purportedly based upon scientific research, our analysis reveals that hardly any research at all is conducted on several of the (commercially) most important species, at least as measured by articles appearing in international scientific journals.
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 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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.113 | 0.223 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".