Plasma concentrations of thyroxine in dairy cows exposed to 60 Hz electric and magnetic fields
Bibliographic record
Abstract
Two experiments were carried out to assess the effects of electric and magnetic fields (EMF) on blood thyroxine (T4) in dairy cattle. In experiment 1, 16 lactating pregnant Holstein cows were exposed to 10 kV/m, 30 microTesla (microT) EMF. The animals were divided into two groups of eight animals each. Each group was exposed to EMF according to one of two treatment sequences of three periods of 28 days each. Sequence 1 was EMF OFF-ON-OFF and sequence 2 was EMF ON-OFF-ON. During the last day of each treatment period, blood samples were collected every 4 h for 24 h to estimate T4 plasma concentrations. In experiment 2, 16 nonlactating, nonpregnant, multiparous Holsteins were exposed to 10 kV/m, 30 microT EMF. The animals were divided into two groups of eight animals each. Each group was exposed to EMF according to one of the two treatment sequences described above, except that each period amounted to the number of days corresponding to one estrous cycle. During treatment, blood samples were collected every other day for T4 analysis. In both experiments, the light cycle emulated a short photoperiod (8 h light/16 h dark). During the ON periods, the animals were exposed to EMF for 16 h, 8 h of the light period plus the first 8 h of during the dark period. In experiment 1, exposed animals did not have any change in T4 plasma concentrations due to treatment (P = .0968), but, the time of sample collection revealed a significant difference (P = .0012). In experiment 2, the effect of period (P = .0009) and the treatment by days interaction (P = .0003) were statistically significant. We conclude that a worst case scenario exposure of dairy cattle to 10 kV/m, 30 microT EMF influences, in a moderate fashion, the blood levels of thyroxine.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".