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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it