Characters analysis of genetic improvement at the males population from Romanian Mioritic Shepherd Dog breed
Bibliographic record
Abstract
The aim of this paper was to analyze, within a group of 26 males from Romanian Mioritic Shepherd Dog breed, 13 characters of genetically improved, characters stipulated in, „Selection sheet and body measurements for Romanian shepherds". The animals were registered with the Romanian Mioritic Association Club from Romania. Romanian Mioritic Shepherd Dog, was selected from a natural population breed in Carpathian Mountains. In order to develop a genetic improvement program at this effective of 26 males from Romanian Sheperd Dog breed, found in evidence of Romanian Mioritic Association Club from Romania, should be considered the following conclusions on variance those 13 characters studied in this paper, respectively, the variability was middle for the width of skull and ear and low for the other 11 characters analyzed. Also, this paper highlighted the following reports of the characters analyzed at the males taken in the study: the ratio between the average of length and width skull was 1.005:1, the ratio between the average of length skull and the average of length muzzle was 1.31:1 and between average of the width of skull and the muzzle was 1.82:1. By Comparing between them length, width and depth of muzzle, resulted a ratio of 1.38:1:1.10.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".