A review of the nutritional requirements of chinook salmon (Oncorhynchus tshawytscha)
Bibliographic record
Abstract
<p>Chinook salmon (<i>Oncorhynchus tshawytscha</i>), also referred to as King salmon, is a premium aquaculture species farmed in New Zealand and to a small extent in western Canada. Despite the high research investment in the 50s and 60s, literature on nutrition on chinook salmon from recent years is sparse, particularly when compared to other salmonid aquaculture species, such as Atlantic salmon (<i>Salmo salar</i>) and rainbow trout (<i>Oncorhynchus mykiss</i>). To improve fish productivity, it is essential to provide specific and efficient diets based on satisfying chinook salmon requirements for nutrients and energy and adapting these to life-stage, season, and production variables. Diet specifications for chinook salmon have to date largely been based on knowledge derived from other salmonid species and may not be optimised for chinook salmon. Thus, there is a lack of knowledge in some fundamental areas. This review explores the production status and literature related to the macro and micronutrient requirements, and general nutrition of chinook salmon. In doing so it compares these requirements to other farmed salmonids to understand some of the differences between species and highlights the main gaps in the literature.</p>
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".