LINK BETWEEN OUTLINE EVALUATION AND BLOOD RELATIONSHIP COUNSINLY BREED, FROMING ANIMAL GENOTYPE
Bibliographic record
Abstract
When developing Ayshire breed herds, we used breeding population of related breed: FAY – Finnish Ayshire, SRB – Swedish red, NRF – Norwegian red, CANAY – Canada red, ORDM – Danish red, the research on influence of their blood relation on exterior characteristics of cows is actual. Studied livestock (n=855) has the following blood parts: 56,5±0,55; 12,9±0,31; 10,7±0,16; 17,8±0,60; 0,7±0,08 % consequently. Classes according to blood part, %: 0.0; 0,1 - 12,5; 12,5 - 24,9; 25,0 - 37,4; 37,5 - 49,9; 50,0 - 62,4; 62,5 - 74,9; 75,0 - 87,4; more than 87,5. Blood relationship according to CAN have a positive impact on udder evaluation (+0,130ххх), general view (+0,155ххх), final (+0,164ххх) and identification mark UDC (+0,119ххх), but negative blood relationship according to FAY on general view (-0,138ххх), according to SRB and NRF breed – on udder evaluation (-0,163ххх; -0,111ххх) and final (-0,133ххх; -0,100хх). Difference between force coefficient influence of blood relationship on exterior features according to FAY and CAN ranges from 7,9 to 18,7 units, and on UDC and FLC s equal to 6,6 and 3,5 units. Joint effect of blood relationship according to FAY and CAN is lower on lineal feature, and on exterior indices it increases. For improvement of individual exterior features account must be taken of blood relationship element at proband according to particular related breed of Ayshire group of diary cattle, focusing on blood element on FAY and CAN and their combinations.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".