New AABP Officers and Directors 2017-2018
Bibliographic record
Abstract
He teaches in the food animal medicine, pharmacology, clinical pharmacology, and feedlot production medicine courses.Research interests include evidence-based drug use in food animals, antimicrobial resistance, and drug residues.Mike's professional passion is to navigate the tension between research data and clinical experience in order to help practitioners deliver well-conceived therapeutic options to their clients and patients.In this capacity, he interacts with practitioners and researchers across beef, dairy, swine, and poultry production.A similar role has evolved in interacting with regulatory agencies, legislators, and food companies in an attempt to prevent a precautionary approach from needlessly removing tools which allow us to protect the health of the livestock resources we care for.Past roles in the AABP have included membership in the Committee on Pharmaceutical and Biologic Issues and beef session meeting planning.Mike has served as president of the Academy of Veterinary Consultants and the American College of Veterinary Clinical Pharmacology, as well as on antimicrobial use committees for the AVMA.His most recent appointment was to the Presidential Advisory Council on Combating Antibiotic Resistant Bacteria.Mike's other life is as a hopeless gear head, with history in drag racing, truck pulling, and off-road vehicles.The "wall of breakage" in the shop serves as a story board for this highly profitable investment, which has led to many memorable moments with the family.Mike also serves as a supervised laborer on their small farm currently dedicated to intensive rotational grazing.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".