Bench to Shop™: An Interdisciplinary Training Program for Transitioning of Transboundary Animal Disease Research to Commercialization
Bibliographic record
Abstract
Transboundary animal diseases (TADs) are livestock diseases characterized as highly contagious, fast-spreading, and capable of producing high morbidity and mortality. Accidental or intentional introduction of these diseases into the United States could devastate the economy, food security, and public health. Training of researchers, scientists and animal health workers is often limited to prevention and diagnosis with little emphasis on the importance of translating knowledge to the development of new products for the prevention, detection and control of outbreaks. The Bench to Shop™ training program was developed to fill this gap and applied an innovative blended-learning method through the use of an online platform, a 3-week experiential training, and a 1-month follow-up project. The program specifically targeted next-generation researchers, including PhD students, post-doctoral researchers, and early-career faculty. A total of 17 trainees, in two cohorts, were selected through a national and international recruitment process. Program evaluation consisted of focus groups, follow-up interviews, and pre- and post-tests of didactic material, revealing statistically significant gains in knowledge. Participants expanded their professional networks with leaders in industry and regulatory agencies related to production and/or commercialization of TAD products and deepened their commitment toward keeping our country safe from TADs. Post-program impacts on trainees included advancing products toward commercialization, partnering with connections made through the program, and demonstrating dedication to homeland security by pursuing product development related educational and career opportunities. Overall, results suggest this program provides an added value and should be readily available to the current and future workforce.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".