Intramammary infection at calving following Petrifilm-based selective dry cow therapy
Bibliographic record
Abstract
For producers motivated to reduce the use of antimicrobials on their farms, teat sealant-based selective dry-cow therapy (SDCT) offers an alternative to blanket dry-cow therapy (BDCT). Under a SDCT program, antimicrobial dry-cow preparations (DCT) are reserved for cows that are suspected, or known, to have an intramammary infection (IMI) at dry-off. Internal teat sealants (ITS) are a non-antimicrobial treatment that have been shown to be protective against new IMI during the entire dry period. Internal teat sealants are recommended for all cows as they enter the dry period, but their role is particularly important for cows not selected to receive DCT in a SDCT program. Determination of a cow's IMI status at dry-off is essential to the success of any SDCT program. Petrifilms are culture media plates that allow producers to culture milk on-farm, with results available in 24 hours. Petrifilms had a sensitivity of 85.2% and a specificity of 73.2% when used to diagnose IMIs in low somatic cell count (SCC; < 200,000 cells/mL) cows at dry-off. The objective of this study was to compare IMI at calving between cows that received BDCT and cows selectively treated on the basis of Petrifilm culture results.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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, 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".