ICOTS-7, 2006: de Mello (Poster) A PRACTICAL EXAMPLE CONERNING DOG BREEDING FOR STATÍSTICAL CLASSROOMS
Bibliographic record
Abstract
In this poster we describe and analyze a practical example for teaching statistics that can be used in veterinarian medicine (health) courses. The example is an investigation concerning dog breeding. In this project we compared three dog breeds: Rottweiller (a large dog), Labrador (a middle sized dog) and Poodle (a small dog). The initial idea was to study the litters of these breeds: several variables were recorded and characteristics noted including the number of new born puppies in a litter, how many of them were female or male, puppy colour, characteristics of the father, the number of dead animals in a litter and average cost of each litter. We compared the averages and dispersion for these variables in order to identify possible significant differences among the breeds. Students find this approach very motivating and interesting. The research about dog litters can be introduced to the student in a constructivist approach (Vygotsky, 1978) to teaching. This type of example can also be used in other courses involving topics such as genetic and animal improvement. If the research is extended to further dog breeds it can be shown to utilize many
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.278 | 0.157 |
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".