Amount of Progesterone Consumed Based on Varying Fat Concentrations, Dietary Recommendations, and Estimated Safe Levels in Commercial Cow Origin Liquid Dairy Products
Bibliographic record
Abstract
Federal guidelines recommend that food with hormone content fall below 1% of endogenous production in the subset of the population with the lowest daily production. The majority of dairy products are obtained from pregnant cows, which increase the level of hormones present. The purpose of this article was to perform theoretical evaluation of the quantity of progesterone in cow's milk based on fat percentages and to assess whether this was within the recommended range. Daily recommended dairy product intake from various countries worldwide was researched. This was compared to the concentration of progesterone previously identified in varying fat contents of cow origin milk to assess the amount of progesterone (mg/day) that would be consumed if the guidelines were followed. The maximum daily progesterone consumption suggested will be met by ingestion of 2.7 L, 1.42 L, 1.13 L, 940 mL, 810 mL, 650 mL of 0% (skim), 1%, 2%, 3.25%, 10% (cream), and 35% (whipping cream) fat liquid cow origin dairy product, respectively. Therefore, ingestion of the highest amount of recommended daily dairy intake fell below 1% of the daily endogenous quantity produced, except in the unlikely case of consumption of 650 mL of 35% fat. Studies demonstrating an effect of cow's liquid dairy product intake may need to be revisited, since levels of progesterone consumption remain within the recommended levels. However, it should be considered that ingestion of cow's milk might have a potential effect on the hormonal profile in patients; however, this seems unlikely.
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 | 0.000 | 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".