Case Study
Bibliographic record
Abstract
A 100-cow dairy herd was investigated for chronic milk fat depression. Implementation of traditional measures to reduce the impact of altered rumen fermentation by increasing physically effective fiber intake only partially alleviated the problem. A field study was then performed to quantify the effect of reducing polyunsaturated fatty acid intake on milk fat and milk fatty acid profile. A total of 22 cows randomly selected in the herd were enrolled in the study. Dietary intake of linoleic acid was decreased from 0.609 lb (277 g) to 0.519 lb (236 g) per cow per day by modifying the ration. The effect of ration reformulation was analyzed in linear regression models using repeated measures within cows. Milk production, milk composition, and milk fatty acid profile data were considered the outcomes. Ration reformulation was associated with an increase of 0.3 percentage points of milk fat (P<0.01) and a concomitant decrease of trans-10, cis-12 conjugated linoleic acid in milk (P<0.001). Results from this case study indicate a small reduction of polyunsaturated fatty acid intake may mitigate milk fat depression in dairy cows.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".