An Animated Model of Reticulorumen Motility
Bibliographic record
Abstract
Understanding reticulorumen motility is important to the assessment of ruminant health and optimal production, and in the recognition, diagnosis, and treatment of disease. Accordingly, the teaching of reticulorumen motility is a staple of all veterinary curricula. This teaching has historically been based on written descriptions, line drawings, or pressure tracings obtained during contraction sequences. We developed an animated model of reticulorumen motility and hypothesized that veterinary students would prefer use of the model over traditional instructional methods. First-year veterinary students were randomly allocated to one of two online learning exercises: with the animated model (Group A) or with text and line drawings (Group B) depicting reticulorumen motility. Learning was assessed with a multiple-choice quiz and feedback on the learning alternatives was obtained by survey. Seventy-four students participated in the study, including 38/42 in Group A and 36/36 in Group B. Sixty-four out of 72 students (89%) responded that they would prefer use of the animated model if only one of the two learning methods was available. A majority of students agreed or strongly agreed that the animated model was easy to understand and improved their knowledge and appreciation of the importance of reticulorumen motility, and would recommend the model to other veterinary students. Interestingly, students in Group B achieved higher scores on examination than students in Group A. This could be speculatively attributed to the inclusion of an itemized list of contraction sequences in the text provided to Group B and failure of Group A students to read the text associated with the animations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".