Are nutritional adaptations of wild deer relevant to commercial venison production$
Bibliographic record
Abstract
This article emphasizes the nutritional management of farmed deer rather than of herded or hunted deer. The objectives of supplemental feeding of farmed deer vary widely and include taming and controlling animals, increasing productivity, improving product quality, increasing carrying capacity, bridging seasonal pastures, improving pasture utilization, and correcting nutrient or mineral deficiencies. When the cervid industry emerged in the 1970s, naturalistic information on ecology and nutrition of wild deer provided a basis for selecting areas for fencing and designing feeding programs and diets. However, deer were found to be adaptable and responded well to intensive management. Growing dependence on improved pastures led to attempts to shift natural metabolic cycles to match pasture growth or to ensure larger (older) calves for fall auctions. Seasonality of production, a characteristic of temperate deer, became viewed as an obstacle to developing markets that seemed to require a year-round supply of fresh product. However, as product values replace speculative values and as product values stabilize, attention will turn to costs of production and to discovering and more fully capturing the natural advantages of the industry. One possible way is to turn seasonal metabolic cycles into an advantage, reducing seasonal maintenance costs rather than encouraging off-season growth. To the same end, interests may turn to co-production and co-marketing of deer and other native and domestic herbivores.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".