Effect of 10 kV, 30 μT, 60 Hz electric and magnetic fields on milk production and feed intake in nonpregnant dairy cattle
Bibliographic record
Abstract
Milk production is the main agricultural income in the province of Québec, and the electrical distribution network traverses the rural dairy production region. This study evaluates the hypothesis that electric and magnetic fields may affect dairy production. Sixteen multiparous nonpregnant lactating Holstein cows (weighing 662 +/- 65 kg and with 150.4 +/- 40 days of lactation) were confined to wooden metabolic crates during the experiment with a 12:12 h light:dark cycle. The cows were divided into two replicates of eight cows each and exposed to a vertical EF of 10 kV/m and an uniform horizontal MF of 30 microT at 60 Hz. Replicate one was exposed for three periods. Each period was represented by an estrous cycle ranging from 24 to 27 days. During the first period, the electric and magnetic fields (E&MF) were off; during the second period they were on; and during the final period, they were off. The second replicate was exposed for three periods also, but the exposure protocol was reversed (first period, on; second period, off; last period, on). Exposure to E&MF (on) resulted in an average decrease of 4.97, 13.78, and 16.39% in milk yield, fat corrected milk yield, and milk fat, respectively; and an increase of 4.75% in dry matter intake.
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 | 0.000 | 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".