Neutron activation analysis and X-ray Rayleigh and Raman scattering of hair and nail clippings as noninvasive bioindicators for Cu liver status in Labrador Retrievers
Bibliographic record
Abstract
The heritability of chronic hepatitis in the Labrador Retriever is studied with the aim of identifying the related gene mutation. Identification of cases and controls is largely based on instrumental neutron activation analysis (INAA) Cu determination in liver biopsies. The burden for these companion animals may be reduced if nail clippings and hair (fur) could serve as a noninvasive indicator for the hepatic Cu concentrations. No correlation was found between hepatic Cu concentrations and Cu concentrations in hair and nail samples. However, hair and nail samples were also analyzed by X-ray tube excitation, taking advantage of the X-ray Compton, Rayleigh, and Raman scattering which reflects the organic components such as the type of melanin. Principal component analysis provided first indications that some differentiation between healthy and sick dogs could indeed be obtained from hair and nail analysis. Principal component analysis of scattered region of x-ray fluorescence spectra of Labrador dog nails, demonstrating the differentiation towards dogs with high and low Cu liver levels (respectively positive and negative PC2 values) reflecting hepatitis, as well as gender (PC1: negative values for female and positive values for males)
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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 | 0.000 |
| 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".