Teaching Animal Genetics and Breeding: What are the resources available to instructors?
Bibliographic record
Abstract
The dynamism of education requires that teaching and learning follow suit. The globalization of education community has made materials for teaching and learning easily accessible but sometimes being aware that these resources exist is challenging. The present resource manual attempts to address this by highlighting some of the teaching and learning resources that are available to the instructor in animal genetics and breeding. The availability of the Internet has improved the access to teaching resources that will be of assistance to teachers and learners. Taking cognizance of this rich resource, a teaching resource portfolio relevant to the animal genetics and breeding has been developed. The portfolio includes some textbook resources for improvement of general teaching skills and course development; educational visual resources such as videos and internet sites: publishing companies that provide teaching texts as well as ancillary teaching resources; some journals relevant to animal genetics and breeding; and brief descriptions of the items contained in the portfolio. Some recommended articles focusing specifically on current issues in teaching that facilitate the process of learning for students as it may pertain to the animal genetics and breeding classroom as well as online sites of relevant humor content resources are provided. As such, this teaching resource manual is timely in providing instructors in animal genetics and breeding with the links to important teaching resources.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".