CHARACTERISTICS OF ROMANOV SHEEP USING HIGH DENSITY DNA CHIPS
Bibliographic record
Abstract
Согласно официальным данным, в России разводится двенадцать грубошерстных пород овец. Романовская порода значительно отличается по своим фенотипическим (тощий хвост, а не жирный) и пролиферативным особенностям (многоплодность и внесезонный эструс), а также по своему происхождению (европейское) от остальных грубошерстных пород. По многоплодию овцематки романовской породы превосходят другие российские породы и большинство мировых пород в 2,5-3 раза. Благодаря репродуктивным особенностям, романовская порода известна за пределами России: в США и Канаде были созданы ассоциации заводчиков романовской породы: North American Romanov Sheep Association (NARSA) и Romanov | Alberta Sheep Breeders' Association, а во Франции на её основе была получена порода Romane (или INRA 401). Помимо чистопородного разведения, широко распространено скрещивание романовских овцематок с баранами коммерческих мясных пород для увеличения выхода помесных товарных ягнят. Высокие пролиферативные качества романовской породы делают её идеальной материнской формой для создания ресурсных популяций, что было подтверждено экспериментально на ферме ФГБНУ ФИЦ ВИЖ им. Л.К. Эрнста. According to official data, twelve coarse-haired breeds of sheep are bred in Russia. The Romanov breed differs significantly in its phenotypic (skinny tail, not fat) and proliferative features (multiparity and out-of-season estrus), as well as in its origin (European) from other coarse-haired breeds. In terms of prolificacy, Romanov ewes are 2.5-3 times superior to other Russian breeds and most world breeds. Thanks to its reproductive characteristics, the Romanov breed is known outside of Russia: associations of breeders of the Romanov breed were created in the USA and Canada: North American Romanov Sheep Association (NARSA) and Romanov | Alberta Sheep Breeders' Association, and in France the Romane breed (or INRA 401) was derived from it. In addition to purebred breeding, the crossing of Romanov ewes with rams of commercial meat breeds is widespread in order to increase the yield of crossbred commercial lambs. The high proliferative qualities of the Romanov breed make it an ideal maternal form for creating resource populations, which was experimentally confirmed on the farm of the Federal State Budgetary Scientific Institution FRC VIZh named after. OK. Ernst.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".