Harvesting the benefits of nutritional research to address global challenges in the 21st century
Bibliographic record
Abstract
Abstract Over the past 20 years, substantial progress has been made in improving feeds and feeding technologies for most aquaculture species. Notable improvements in feed conversion efficiency (through a better understanding of requirements and improved feed management) and ingredient sustainability (through increased capability to use a wider range of ingredients) have been achieved. While advances have been made in understanding the requirements of many of the main aquaculture species, there is still much to be done in defining requirements, especially for many of the species being farmed in the developing world. Gains in the efficiency of feeds are slowing for developed species, but potential gains are still appreciable for less developed species. There is a growing need to more precisely prescribe the required levels of essential nutrients and various additives in the diet based on age, genotype, environment, and immune status to deliver a “precision nutrition” approach to farming aquaculture species. There is still further need to diversify our ingredient options to provide greater resilience, as the sustainability of different feed ingredient sources, including possible climate change impacts, is becoming a growing issue. There is a growing demand for biocircularity in our feed ingredient supply chains. Ultimately, what is needed to sustain future feed ingredient needs are sustainable sources of cost‐effective protein, some essential amino acid additives, some omega‐3 fatty acid resources, and various minerals and vitamin additives. The increasing use of new and varied resources will ensure that food safety remains an important issue throughout the world. Feed manufacturing has evolved from a simplistic exercise to a highly complex science with state‐of‐the‐art engineering, but its application is not consistent across all sectors, as there is still widespread use of pelleting, mash, and trash fish feeding in the developing world. Similarly, feed management has also dichotomized between the developed and developing world, with a high reliance on manual skilled labor in the developing world, whereas more advanced aquaculture systems are becoming increasingly reliant on automated computer‐controlled feeding systems.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".