Vertically Integrated Educational Collaboration between a College of Veterinary Medicine and a Non-profit Animal Shelter
Bibliographic record
Abstract
The College of Veterinary Medicine and Biomedical Sciences (CVMBS) at Texas A&M University (TAMU) has developed a multifaceted program in partnership with the Brazos Animal Shelter to provide teaching opportunities with shelter animals during all four years of the professional curriculum. In the first three semesters of the professional program, students working in small groups spend two hours per semester at the shelter performing physical examinations, administering vaccinations and anthelmintics, completing heartworm or FeLV/FIV testing, and performing simple medical treatments. In an expanded fourth-year program, groups of six students spend 16 contact hours at the shelter during two-week rotations, completing similar tasks. Through this program, each student practices animal-handling skills and routine procedures on an average of 150 to 200 dogs and cats. In addition, during third- and fourth-year surgery courses, student teams spay or neuter an average of 12 to 18 dogs or cats each week. More than 800 animals are spayed/neutered annually through this program, and each student directly participates in 12 to 15 spay/neuter survival surgeries. The program represents a creative approach to veterinary training that conscientiously uses animal resources in a positive fashion. We believe that this is a successful partnership between a state-supported veterinary college and a non-profit shelter that benefits both agencies. We encourage other veterinary colleges to explore similar partnership opportunities to provide optimal training for professional students while using animal resources efficiently.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".