Exposure of pregnant dairy heifer to magnetic fields at 60 Hz and 30 µT
Bibliographic record
Abstract
Thirty-two pregnant Holstein heifers weighing 499 +/- 45 kg, at 3.1 +/- .7 months of gestation and 21 +/- 2.0 months of age were confined and exposed to 30 microT magnetic fields (MFs) and a 12 h light/12 h dark light cycle. The heifers were divided into two replicates of 16 animals. Each replicate was divided into two groups of eight animals each, one group the non-exposed and the second, the exposed group. The animals were subjected to the different treatments for 4 weeks. After 4 weeks, the animals switched treatment, the exposed group becoming the non-exposed group and vice versa. Then the treatment continued for 4 more weeks. Catheters were inserted into the jugular vein, and blood samples were collected twice a week to estimate the concentration of progesterone (P4), melatonin (MLT), prolactin (PRL), and insulin-like growth factor 1 (IGF-1). Feed consumption was measured daily. The results indicated that exposure of pregnant heifers to MF similar to those encountered underneath a 735 kV high tension electrical power line for 20 h/day during a period of 4 weeks produces slight effects. This is evidenced by statistically significant higher body weight (1.2%), higher weekly body weight gain (30%), and decreases in the concentration of PRL (15%) and IGF-1 (4%) in blood serum. The absence of abnormal clinical signs and the absolute magnitude of the significant changes detected during MF exposure, make it plausible to preclude any major animal health hazard.
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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) | 0.002 | 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".