Exploring the Dog Diversity of District Buner, Pakistan
Bibliographic record
Abstract
Objective: The present study was documented to explore the dog diversity and different types of Dog breeds in district Buner. Methods: The current study was carried out from August 2021 to August 2022. During the study 11 different types of breeds were identified in district Buner. Results: The identified species of Dog in district Buner consist of German shepherd (Canis lupus familiaris), Pointer dog (Canislupus), American eskimo dog (Canis lupus), Siberian huskey (Canis lupus familaris), Belgium shepherd (Canis lupus), Bull terrier (Canis lupus), American Labrador (Canis lupus familaris), Toy poodle (Canis lupus familiaris), Gull doing (Canis lupus), Stray dog (Canis lupus domesticus) and vikhan sheep dog (Canis lupus domesticus). Conclusion: About 40 different specimens of dogs were collected in each tehsil. During the investigation Stray dogs were the most dominant breed followed by German shepherd.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".