Bibliographic record
Abstract
Abstract Consumers expect selection in almost every category of purchase including protein from meat sources. There are a variety of meats consumed worldwide with sustained marketing efforts for many species. This presentation will serve as an introduction to the topic of meat proteins from novel species, consumption trends and importance of understanding characteristics of specific meats. Bison, goat, ostrich, elk, deer, yak, water buffalo, horse, donkey, rabbit, wild boar, deer, alligator and llama are all consumed in various quantities in different parts of the world. Understanding reasons for meat selection and the many factors that can impact meat quality can help align production and marketing decisions for optimal consumer satisfaction. Animal genetics, feed, pre-slaughter handling and transport, proper stunning, carcass chilling, cold chain continuity and preparation of the meat can all influence the end eating experience. Research into meat characteristics of various species supports marketing efforts and puts integrity on nutritional claims. An example of this is bison meat marketing which promotes a lean, healthy meat raised on sustainable production systems in Canada and the USA with a rich history behind it. Nutritional analysis of bison meat and production research helps support these claims. Investigation into characteristics of meat from several species throughout the world could broaden opportunities for successfully marketing them as niche products.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.437 | 0.189 |
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".