Kelp breeding in China: Challenges and opportunities for solutions
Bibliographic record
Abstract
Abstract Breeding has played an important role in the mariculture and industrialization of kelp in China. However, the current kelp breeding systems in China have encountered some problems relating to germplasm diversity, management, technological innovations, and regional co‐operation. This review summarizes the main challenges, such as top‐down and fragmented management of germplasm libraries, as well as private industry breeding without government regulations, inter‐cultivar accidental admixing and genetic erosion, loss of heterozygosity due to repeated selection and self‐crossing. We outline multiple potential approaches to breed cultivars with improved qualitative/quantitative traits which can be subjected to changing environments, for example: (i) establishing a national germplasm repository to enhance integrative collection and preservation of kelp resources; (ii) planning and implementing kelp breeding programmes according to strategic priorities and goal‐orientations; (iii) optimizing a hybridization‐based breeding pipeline to produce robust cultivars through the introgression of novel alleles and thus the expression of hybrid vigour; (iv) enriching the high‐quality annotated reference genomes and functional analysis of trait‐associated markers/loci to develop DNA‐based breeding technologies; (v) developing new priming‐based (e.g., thermal and disease resistance) bio‐engineering breeding strategies to meet future unpredictable climate change; and (vi) breeding towards an ecological kelp‐microbiome interaction‐based technique to produce cultivars with enhanced performance and adaptability to environmental scenarios. Collectively, the lessons learned from kelp breeding in China and the solutions proposed here may not only potentially improve or re‐invigorate the Chinese kelp industry, but will also assist other developing countries in taking corrective actions to develop a sustainable future kelp farming industry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".