Bibliographic record
Abstract
The infertility of milk cow is a world problem with high incidence rate. Reportedly, in the world dairy industry, theproportion of the infertile milk cow was about 15% in the total mature cows (Donald L B, 1978, P.309), and someothers thought this number achieved about 30% according to the statistics (Bulman D C, 1980, P.177-188). In US, thereare 12%~19% cows which are eliminated through selection because of sterility and breeding diseases every year, andthis number achieves above 40% in all unqualified cows (Jiang, 1990, P.38-41). In China, the infertile rate of maturecows achieves above 25%, and the rate in some cattle farms with imperfect management and bad technical conditionseven achieves above 40% (Jiang, 1990, P.38-41). According to the statistics, in a cattle village in the northeast, therewere 62 infertile cows in 212 cows, and the infertile rate achieved 29.25%, and in the region of Jinan, Shandong, therewere 342 mature cows in 5 collective cattle farms, one civil cattle farm and one cattle breeding village, and the amountof the infertile milk cow achieved 97, i.e. 28.36% of the total amount of mature cows. In the region of Shihezi inXinjiang, the infertile rate was 22.5%, and the infertile rate in Fujian Province achieved above 10%. In recent years,according to Chinese traditional medicine, Chinese veterinary scientists have accumulated abundant experiences toprevent and cure the infertility of milk cow, and explored the pharmacology of the function of Chinese traditionalmedicine.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".