Reaching Beyond Our Walls: Library Outreach to Veterinary Practitioners
Bibliographic record
Abstract
The Texas A&M University Medical Sciences Library (MSL) supports lifelong learning for Texas veterinarians and College of Veterinary Medicine and Biomedical Sciences (CVMBS) alumni through several ongoing outreach efforts. The MSL provides free document delivery and literature search services to practicing veterinarians in support of patient care. The MSL also responded to unique opportunities to expand services and increase its visibility through collaborations with the American Association of Equine Practitioners and CABI, provider of VetMed Resource. The MSL continues to explore ways to expand its mission-critical veterinary outreach work and market library services to veterinarians through participation in continuing education, regional meetings, and veterinary student instruction.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".