Bibliographic record
Abstract
Abstract Gary Allee’s professional career as a swine nutritionist spans from 1972 to 2020 in the peer-reviewed publication record. An evaluation of the major themes presented in his publications show a connectivity of work done at a “basic” science level leading to work on the application of this basic knowledge to meet pig production needs. It is interesting to explore the themes of Dr. Allee’s work and connect that to how pigs are fed on a practical basis today. Regarding sow nutrition work, Dr. Allee used his early work on lipogenesis to expand on the topic to understand how energy sources are able to cross the placental barrier and ultimately impact neonatal energy reserves. Further, Dr. Allee responded to availability changes in synthetic amino acids to the industry to explore opportunities to leverage these AA’s into sow diet formulations. In the area of nursery pig nutrition, his record explores the theme of lipid metabolism from lipogenesis, to ketogenesis to fat source and fatty acid profile implication to diet value. However, Dr. Allee picked up the themes of protein and lactose sources and implications on hypersensitivity and value of proteins and sugars targeted to the young pig all influencing practical diet formulations globally in young pig nutrition. In the area of the grow-finish pig, Gary’s work explored topics of dietary fat use, practical aspects of amino acid inclusion and the interaction of repartitioning approaches to lean deposition with nutrient requirements. This symposium is designed to highlight several key principals for success as a scientist that are evident in Dr. Allee’s career: a) importance of being a systems thinker in ability to take basic information and evolve it into practical implications; b) importance of being a global citizen in scientific professional development; and c) the impact that one has on people endures.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.019 |
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".