Bibliographic record
Abstract
High levels of fish mean cannot be used in chick diets in the United States because of the relatively high cost as compared to soybean meal. In certain areas or countries where large amounts of fish meal are produced, it may be economically feasible to use fish meal as the major protein supplement in the poultry rations. Feed consumed by poultry must provide most of the materials the birds need for growth or to produce eggs. Those who formulate feed must select ingredients and combine them in proportions which will allow the bird to grow or produce eggs at the lowest possible cost. Fish meal is a good source of essential amino acids. Numerous studies during the past several decades have demonstrated the value of fish meal as a source of unidentified growth factors, essential amino acids, vitamins, minerals, and energy. The nutritive value of commercially produced fish meal showed great variation when investigated by the protein quality index method. The fish species used, the parts of fish used, amount of heating, fat removal, and duration of storage significantly affected the nutritional value of fish meals. The methods of processing and fractionation of the original fish carcass are different for different species. For example, menhaden meal is made from whole fish, but tuna fish meal is made from cannery scraps that contain relatively less muscle and more bone and skin than does menhaden meal. Meal high in bone and skin may be inferior to meals high in muscle protein. Meals high in oil may be poorer than those lower in oil if the meals are not processed and stored properly. The experiments reported in this thesis were conducted to determine if the three fish meals studied varied in the amino acid levels and pattern provided to the chick and to determine if these differences explain differences in the feeding value of these meals. Studies were conducted with two lots of Canadian herring meal, a tuna meal, and Peruvian fish meal probably made from anchovy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".