Management practices on organic and conventional dairy herds in Minnesota
Bibliographic record
Abstract
The objective of this study was to describe and compare husbandry practices on organic and conventional dairy farms of similar sizes in Minnesota. Organic (ORG, n=35), same-sized conventional (SC, n=15, <200 cows) and medium-sized conventional (MC, n=13, ≥200 cows) dairy herds were visited in 2012, and farmers were interviewed once about their farm, herd demographics, and herd management practices concerning nutrition, housing, and reproductive programs. Organic farms had been established as long as conventional farms, and ORG producers had most commonly selected ORG farming because of a negative perception of pesticides for human health. The distribution of cattle breeds and ages differed across farm types. Organic farms had more crossbred cows and a greater number of older cows than conventional farms, who had mainly Holstein cattle. Organic farms did not dock tails, were more likely to use breeding bulls, and were less likely to conduct pregnancy diagnoses in cattle. All conventional farmers fed corn, corn silage, and hay, but no forage or feed supplement was fed by all ORG farms with the exception of pasture. Kelp was supplemented on most ORG farms but on none of the conventional farms. In summary, although there were differences across farm types regarding the use of pasture, feeds, and feed additives, breed and age distribution, reproductive management, and the use of tail docking, observations in other management areas showed large overlap across herd types.
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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 | 0.000 | 0.000 |
| 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".