Characterization, registration and gazette notification
Bibliographic record
Abstract
The first animal to be domesticated by the man was dog which probably occurred at least 14,000 years ago- the animals being used for hunting and as watchdogs. In India many exotic dog breeds (mainly German Shepherd, Labrador, Doberman, Pomeranian etc) are kept as pet but little emphasis has been given to the native dog breeds. Some Indian breeds of dogs reported in India are Caravan Hound, Combai, Chippiparai, Rajapalayam, Rampur Hound, Kanni, Mudhol Hound, Indian Mastiff (Bulli), Himalayan sheep dog, Bhutia dogs, Bhakarwal dogs etc. ICAR-NBAGR has developed methodology for characterization of dog breeds of Indian through systematic survey in the area of its distribution along with Breed Descriptors format for dog breeds. Phenotypic characterization of two indigenous dog breeds viz Rajapalayam and Chippiparai were completed by ICAR-NBAGR. Rajapalayam and Chippiparai dogs are exclusively reared for guarding the farm and farm houses. Chippiparai dogs are even given to brides during marriage. There is huge demand for the pups of these dog breeds. State of Karnataka has also completed the characterization of Mudhol hound breed. All three dog have been registered by ICAR & Gazette notified by Govt. of India.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.144 | 0.075 |
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".