The Use of Different Statistical Metrics to Detect Physiological Changes in Metal-Overloaded Avian Bone
Bibliographic record
Abstract
Slag from nickel smelting operations in the Sudbury basin in Ontario, Canada, has become ubiquitous. This material rich in heavy metals - particularly iron - upon ingestion has the potential to effect changes in physiology. In previous work, we analyzed the effects of dietary slag ingestion on several quantitative parameters of the tibio-tarsal bones in pigeons, including density, cortical thickness, bone mineral density, calcium and iron concentrations, Youngs Moduli, and breaking strength. In the present work, we compute the Pearson correlation coefficients for all possible sets of bivariations of the measured parameters for a control group fed a normal diet, and an experimental group fed a slag-based diet. Furthermore, the Pearson distance, which is a metric associated with the degree of clustering between two independent sets of bivariate correlations, was calculated for all corresponding pairs of bivariations. A new metric - the Distance correlation R - is a measure of multivariate correlation which tests for any type of correlation, including linear correlation (or anticorrelation). In analogy to the Pearson distance, we introduce in this work the Distance correlation d<sub>R</sub>, and a criterion for its statistical significance. On the existing data set, the Distance correlation R was also calculated for all 42 bivariations, and the associated 21 values of d<sub>R</sub> were computed. In the control group, the Pearson correlation revealed an anticorrelation between cortical thickness and breaking strength. In the experimental group, the metric revealed a correlation between breaking strength and bone mineral density, and an anticorrelations between: i) Youngs Moduli and iron concentration, and ii) between cortical thickness and bone mineral density. Significant Pearson distances between: i) cortical thickness vs. breaking strength, ii) cortical thickness vs. bone mineral density, iii) density vs. bone mineral density, and iv) breaking strength vs. bone mineral density, were indicative of fundamental changes in bone physiology in the experimental group. The Pearson distance metric was thus effective in detecting physiological changes associated with the presence of metal overload in bone. The Distance correlation R revealed a significant correlation between cortical thickness and breaking strength in the control group, and between the calcium and iron concentrations in the experimental group. The d<sub>R</sub> metric revealed significant loss in correlation between calcium concentration and bone mineral density in the experimental group. In summary, the statistical metrics used - and introduced in this work - are efficacious in detecting physiological changes in metal-overloaded bone.<div><br></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".