Evaluation of the early conception factor dip stick test in dairy cows between days 11 and 15 post-breeding
Bibliographic record
Abstract
A field study was conducted between February and November 1995 using 139 Holstein cows to determine the accuracy and the usefulness of the ECF Dip Stick Test performed between days 11 and 15 post-breeding. Results of the ECF tests were compared to pregnancy diagnosis after 25 days post-breeding using ultrasonography and transrectal palpation. The apparent conception rate of the study population based on transrectal palpation was 38%. The ECF test sensitivity, specificity, positive and negative predictive values were 81%, 26%, 40% and 69%, respectively. The Kappa value (0.06; 95%CI -0.19 to 0.30) demonstrated no agreement between the ECF test and the final pregnancy status of dairy cows. These results would indicate that the ECF test is not a good predictor of pregnancy because the proportion of false positive results was high at 46% (64/139). Furthermore, the accuracy of the test to detect open cows is not acceptable with the high proportion of false negative tests of 31% (10/32). This finding would indicate that 19% (10/53) of the cows diagnosed pregnant rectally were misdiagnosed as non-pregnant by the ECF test. Finally, many factors such as the incubation time, the personal assessment of the test reaction and the source of light can also affect the ECF test reading. Based on the current technology, the authors would not recommend the use of the ECF test to help dairy producers reduce non-productive days.
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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 | 0.002 | 0.005 |
| 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) | 0.000 | 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".