Environmental and Genetic Factors Shaping the Global Expansion of Tilapia Aquaculture
Bibliographic record
Abstract
As an important farmed fish in the world, tilapia has been widely cultivated in dozens of countries around the world due to its advantages of strong adaptability, rapid growth and low breeding costs. This study summarizes the effects of environmental and genetic factors on the expansion of tilapia farming territory. First, tilapia exhibits excellent ecological adaptability and has ecological plasticity such as wide temperature and salt. It can grow and reproduce normally under different temperature and salinity conditions, and adapt to high-density breeding and low-oxygen environments through regulating physiological mechanisms. Global climate change is changing the suitable areas for tilapia. Rising temperatures have extended the tilapia farming map to high latitudes, and is expected to further expand in tropical and subtropical areas, but extreme environments may bring new breeding risks. Genetic improvement plays a key role in the optimization of tilapia species. The application of high-growth strain breeding, disease-resistant molecular breeding and gender control technology has greatly improved the production performance and stress resistance of tilapia. Through case analysis of the development of tilapia industry in typical countries such as China, Egypt, and Brazil, the impact of environmental conditions and genetic improvement on industrial layout is revealed. Finally, we will discuss the ecological invasion risks faced by global expansion of tilapia, the environmental pressures of high-density breeding and genetic pollution risks, and look forward to the application prospects of technologies such as precise breeding, gene editing, and intelligent breeding in improving the sustainable breeding of tilapia. This study aims to provide scientific basis and decision-making reference for the global layout and sustainable development of tilapia farming.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".