ECOLOGIA DE ICTIOPLÂNCTON: UMA ABORDAGEM CIENCIOMÉTRICA
Bibliographic record
Abstract
The early life history of fish involves multiple processes and strategies to ensure the survival, and the knowledge on this field provides important information about the conservation of species and fish stocks. We used a scientometric approach to investigate the current situation of ichthyoplankton ecology and to assess the overall scientific production indexed in the Web of Knowledge databank from 1990 to 2015. We additionally analyzed Brazil's position on the world ranking. The information collected covered the country where the study was developed, the different sampled environments (marine or freshwater), and the type of studies developed (predictive, descriptive, experiments and modeling). In addition, we investigated the names attributed to ichthyoplankton (larvae, juvenile, young of the year, etc.) in titles and abstracts of articles. We found 1104 articles published by authors from 66 countries, most of which was classified as descriptive and related to marine environments. Brazil was the fourth most productive country, after the United States of America, Australia and Canada. Switzerland was highlighted by publication of experimental studies and Norway by developing modeling studies. Predictive studies were produced mainly in Canada, Australia and Germany. Globally, the most common topics were "distribution" and "structure of the assemblages," followed by some issues classically focused on ecology and monitoring of fishing, such as "spawning grounds" and "recruitment". Other topics such as "competition", "predation", "anthropogenic impacts" and "biological invasions" were among the least studied. The results allows to suggest challenges for the future of this field in the world: develop broader ecological researches, test hypotheses and try to answer questions that aim current issues widely discussed in other areas, such as global changes, still not addressed by ichthyoplanktologists. In Brazil, research should aim a better understanding of the thousands of native fish species whose environmental requirements during the early life stages remains unknown, but without losing focus on issues of global interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.281 | 0.008 |
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; both teacher heads agree on what is shown here.
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".