Factor analysis of essential and toxic elements in human placentas from deliveries in artic and subarctic areas of Russia and Norway
Bibliographic record
Abstract
Concentrations in human placenta of 11 essential elements (P, Ca, Mg, Cu, S, Na, Fe, Zn, K, Se, Mn) and 5 toxic elements (Ba, Sr, Pb, Ni, Cd) are compared for each of two arctic communities in eastern Norway and western Russia, and for another in each country located at more southerly latitudes. All but Mg, Fe, P and K were present in higher concentrations in the Russian study group. The observed inter-element correlations are reflected by the four major factors identified in a principal component analysis. The total variation explained was 67.3%, of which more than half (35.3%) was contributed by Factor 1. P, Ca, Mg, Ba, Sr, Pb, and Ni were major contributors to this factor. The placental concentrations of these elements depended strongly on gestational age, increasing from about week 35 and peaking near weeks 39 and 40, and exhibited skewed frequency distributions and a dependence on maternal smoking. The gestational-dependent mineralization of the placenta is interpreted to reflect the deposition of metal phosphates coinciding with smoking-induced tissue damage. The loadings of the remaining three factors are reviewed in the context of common uptake mechanisms, similar biochemistries and unique transport pathways. The inter-element relationships and grouping of the elements observed should constitute a scientific base for the use of placenta composition in environmental monitoring and epidemiological studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".