Analyzing descriptive and content structure of scientific documents in the field of fisheries sciences and engineering
Bibliographic record
Abstract
Background and aim: The aim of this study was to analyze the descriptive and content structure of scientific outputs produced by fisheries researchers of Iranian universities in the Web of Science (WoS) from 1990 to 2020. Material and methods: This scientometric study was performed using co-word analysis method. The population of this research consisted of 1755 fisheries documents, published during 1990-2020 and indexed in the WoS so that at least one of the researchers had organizational affiliation with Iranian universities and research centers. The data were analyzed using Excel, and the thematic maps of this area were drawn using VOSviewer. Findings: The findings showed that Iran was ranked 25th in international fisheries scientific outputs; in this list, the United States, Japan and Canada were ranked first to third in the world, respectively. Moreover, the findings indicated that Iran had the highest number of scientific outputs in 2017. Among the universities and research centers of Iran, Islamic Azad University, Tehran University and Gorgan University of Agricultural Sciences and Natural Resources had the highest rate of scientific outputs, respectively. The researchers published the most documents in the Iranian Journal of Fisheries Sciences, Journal of Applied Ichthyology and Aquaculture Research. In the study of the co-word network analysis of Iranian fisheries, 10 thematic clusters were identified. Conclusion: Social network analysis of word concepts has suggested that the highest centrality indicators of fisheries studies is associated with marine biology, aquatics reproduction, aquaculture, aquatics safety system, quality of food products, pathogens in aquatics, pollution of aquatic environments, physiological indicators of stress, aquatics nutrition and salinity stress.
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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.044 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".