Geographic Patterns of Genetic Structure and Global Gene Flow in Catfish Populations
Bibliographic record
Abstract
This study summarizes the geographical pattern of catfish population genetic structure and global gene flow characteristics, expounds the theoretical basis and technical progress of catfish population genetic research, compares the typical patterns of catfish population genetic structure in different regions such as Asia, Africa and South America, Europe and North America, and analyzes the dynamic mechanisms affecting global catfish gene flow, including geographical and ecological barriers (such as watershed isolation, habitat differences), paleoclimate and geohistorical events, and human activities (such as dams and species introduction). At the same time, it also explores the significance of population genetic structure and gene flow in ecological adaptation, hybridization consequences and speciation, and uses case studies such as Asia (such as the giant catfish in the Mekong River), America (such as the Amazon migratory catfish) and human introduction (such as the invasion of African catfish in Bangladesh) to deepen understanding. This study looks forward to the application prospects of catfish population genetic research, such as genetic resource protection, breeding application, advanced technical means and international cooperation, and provides a reference for population genetic research and resource management of catfish and other aquatic organisms.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".