Stability of bovine milk progesterone under different storage and thawing conditions
Bibliographic record
Abstract
The objectives were to determine the effects of storage temperature (21 or 4°C), a preservative agent (Brotab 10®) (exp. 1), thawing temperature (37, 21, or 4°C), repeated freeze-thaw cycles (exp. 2), and length of storage at -20°C (exp. 3) on the stability of bovine milk progesterone (P4) over an 8-wk period. Whole-milk samples of 19 pregnant dairy cows were analyzed for P4 using an enzymeimmunoassay (Quanticheck®). In exp. 1, mean P4 concentrations declined (P < 0.01) from 0 to 28 d (5.2 ± 0.1 vs. 3.3 ± 0.1 ng mL-1), but not any further at 56 d. However, P4 decline was lower (P < 0.01) at 4°C than at 21°C at 3 and 56 d, respectively (4.3 ± 0.1 and 3.8 ± 0.1 vs. 3.9 ± 0.1 and 3.0 ± 0.1 ng mL-1). Brotab 10® tended (P < 0.08) to reduce P4 decline. In exp. 2, thawing temperature and repeated freeze-thaw cycles, and in exp. 3, the length of storage at -20°C, did not greatly affect P4 stability. Regardless of the temperature, P4 concentrations declined in all experiments by 1.1 ± 0.1 mL-1 in the first 3 to 7 d of storage and remained relatively stable thereafter, except when stored at room temperature in the absence of a preservative agent. In conclusion, P4 in whole-milk samples remained relatively stable for up to 3 d at 21°C and for up to 14 d at 4°C, even in the absence of a preservative agent. For periods longer than 14 d, whole-milk samples are best stored at -20°C for optimum stability of P4. Key words: Milk progesterone, progesterone stability, storage conditions, enzymeimmunoassay
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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, 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".