A Review on Feeding System for Deer Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deer farming has been continuously expanding because it requires less investment, land, animals, and labor than other livestock enterprises. The system of velvet antler production is relatively new, however, it may continue to grow until its full potential. High growth rate and feed availability can enhance deer farming. Understanding the feeding system for deer production is essential in achieving maximum productivity. Deer have mainly been grazed in perennial pasture for venison and velvet antler production in western countries, including New Zealand, Australia, Canada, and America, where they have intensively been fed a variety of food sources for velvet production in oriental countries, including Korea and China. It is well known that deer belong to intermediate eater and have a good feed availability. Furthermore deer show a seasonal physiological digestive system and their feed availability differs seasonally. Deer farming industry in Korea has mainly depended on imported feed sources, such as oak leaf hay and alfalfa bale, owing to small plow land and increased labor fee. However, oak leaf hay which was greatly acceptable by deer farms had a low feed availability and comparatively high cost. Therefore, they demanded increasingly positive development of feed source which can reduce cost and increase availability. Forest by-product which is included trees, wild grasses and shrubs collected from the reforestation areas, agricultural by-products including soybean cured meal and brewer's grain, and forages including corn, sorghum, and rye silages are expected to adapt well for deer. Furthermore, it was proven that there is a possibility to produce high quality velvet antler by feeding several different feed sources including medicinal herbs.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it