States of development and application of genetic and genomic tools in aquaculture and conservation programs: a guide for strengthening dialogue among practitioners of aquaculture and genetics
Bibliographic record
Abstract
Throughout all stages of fish conservation and aquaculture development, genetic and genomic approaches can be leveraged to enhance understanding of the diversity and complexity of these organisms, including the linkage between phenotype and genotype, and their adaptive and breeding potential. These approaches can inform processes ranging from the initial collection of wild broodstock to the ongoing use of genomic selection on domesticated lines. Due to the diversity in cultured fish species, small and medium enterprises (SMEs) commonly explore new species for culture, or work with species within a narrow regional conservation or commercial focus. These enterprises face obstacles in utilising genetic and genomic approaches due to development and implementation costs, specialised skill set requirements, and infrastructure and labour limitations; yet the benefits often outweigh these challenges. Choosing the best molecular genetic or genomic tools depends on programme goals and species, but small and medium enterprises may miss opportunities to acquire more information through their current approaches, or not realise what may be gained through modest investments in genomic tools. To provide better insight and promote discussion and collaboration between culturists and genomic practitioners, we define and describe five States of development and application of genetic and genomic tools frequently observed in aquaculture and conservation breeding programs. We characterise these tools, their general applications, and how current technologies allow programs to advance to higher States without following a sequential progression, a concept we refer to as “State skipping”. This document outlines the available molecular genetic and genomic tools, but does not cover animal breeding or the science behind it. Similarly, bioeconomic models are not included, although relative economic costs and benefits are highlighted. The technical considerations and limitations of various approaches are reviewed, along with available resources for those seeking further support in exploring genetic and genomic tools in breeding programmes.
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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 | 0.029 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".