Response to Letter to the Editor “LSU–SVM Shelter Medicine Program”
Bibliographic record
Abstract
We thank the editors of JVME for contacting us regarding our article, ‘‘Training Veterinary Students in Shelter Medicine: A Service-Learning Community Classroom Technique,’’ originally published online in 2013. Members of the faculty from Louisiana State University (LSU) questioned our statement that we ‘‘believe this program to be unique in the field of shelter-medicine clinical rotations.’’ The issue seems to be one of semantics. We are certainly aware of programs at LSU and other veterinary colleges that focus on shelter medicine and are very glad that such excellent programs exist. It is our understanding that these colleges, including LSU, use a full-service mobile hospital for these rotations. Our intent with this article was to outline a program that would allow colleges without a mobile hospital to see the potential for developing useful programs in shelter medicine that benefit the students as well as the shelters. Our program used a Chevy Suburban fitted with a vet box for travel and service to the outlying shelters—hence, our comment on ‘‘true field service’’ as taken from our large animal counterparts. One of our challenges was that workspace was often makeshift in the shelter itself, and our solution allowed for flexibility and forward-thinking from the students, faculty, and shelter staff. We certainly did not mean to imply that other colleges are not doing fantastic and inspiring work with shelters, and we thank the members at LSU for their letter highlighting the work being done there. We can all work together to raise awareness of the many ways that this important topic can be taught, and to provide students with first-hand experiences. We thank LSU for their interest and continued care of unwanted and abandoned animals.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.028 | 0.032 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".