Reproductive sterility in aquaculture: A review of induction methods and an emerging approach with application to Pacific Northwest finfish species
Bibliographic record
Abstract
Abstract Aquaculture is the fastest‐growing food‐production sector and is striving to become a long‐term sustainable approach to meet the rising global demand for seafood. During the expansion and advancement of aquaculture, minimizing ecological impacts should occur concomitantly with maximizing production. Farmed fish, often genetically distinct from their natural conspecifics, may pose significant risks of genetic contamination and ecological imbalance to wild populations if they escape from aquaculture confinement. Growing reproductively sterile fish is the most effective way to genetically contain farmed fish. Atlantic salmon ( Salmo salar ) escape events in the ‘Pacific Northwest’ region of the United States and Canada have raised alarms over potential ecological impacts and led to legislation in Washington State phasing out the culture of non‐native finfish species. Farming sterile native species such as coho salmon ( Oncorhynchus kisutch ) and sablefish ( Anoplopoma fimbria ) in the Pacific Northwest would ease public concerns and promote environmentally and economically sustainable aquaculture. Sterile fish also can mitigate the challenge of precocious maturation, a prominent issue associated with culture of salmonids and many other species, to improve somatic growth, flesh quality and fish health and welfare. Here, we review methods having potential applications for producing sterile fish and introduce our novel immersion‐based technology that temporarily silences the dead end ( dnd ) gene using Morpholino oligonucleotides to produce sterile coho salmon and sablefish for the first time. The successful induction of sterility in these two iconic Pacific Northwest species without introducing genetic modifications would promote the use of this immersion‐based sterilization technology for more aquaculture finfish worldwide.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".