Core Competencies in Animal Physiology
Bibliographic record
Abstract
The past decade has seen greater emphasis placed on competency‐based education in the biomedical sciences, as evidenced in the calls to reform by AAAS (Vision and Change) and AAMC/HHMI (Scientific Foundations for Future Physicians). A working group of animal physiology instructors, established at the 2014 APS Institute of Teaching and Learning (ITL), formulated a list of ten core competencies required of students to fully master animal physiology. These competencies address core concepts such as body size, life history, phenotypic plasticity, trade offs, adaptations to common environmental challenges, adaptations to extreme environments, and evolutionary constraints. While relevant to human physiology, these concepts are most clearly understood from the perspective of animal physiology. The core concepts were unpacked into constituent ideas and learning objectives specific to each core competency were developed. Comparison to the foundational chapters of several widely‐used textbooks in animal physiology showed substantial consonance but also revealed that coverage of some concepts was scant or lacking. Further refinement of the competencies will continue through consultation with a wider group of instructors in animal physiology. Formulation of the core competencies in animal physiology provides faculty with demonstrable and measurable outcomes for student learning and sets the stage for developing assessment tools designed to evaluate student understanding of the core concepts in animal physiology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".