EVALUATION OF SERUM FERRITIN AND SERUM IRON IN FREE-RANGING BLACK RHINOCEROS (<i>DICEROS BICORNIS</i>) AS A TOOL TO UNDERSTAND FACTORS AFFECTING IRON-OVERLOAD DISORDER
Bibliographic record
Abstract
Iron overload disorder (IOD) is a significant health issue for captive black rhinoceros ( Diceros bicornis ). Measurement of serum ferritin with a validated rhinoceros ferritin ELISA has been used extensively to detect animals in U.S. zoos that are at risk of developing IOD. However, there is limited information on serum ferritin levels in free-ranging black rhinoceros using this same assay. Serum ferritin, iron, and gamma-glutamyl transpeptidase (GGT) were determined in 194 black rhinoceros from southern Africa. Mean ferritin in free-ranging black rhinoceros (290.54 ±247.4 ng/ml) was significantly higher than in free-ranging white rhinoceros (64.0 ± 102.4 ng/ml) sampled in this study from Kruger National Park, South Africa. However, there were no significant differences between genders or age groups. Ferritin values varied with geographical location of the black rhinoceros, although this was not clinically significant. Serum iron values were also higher in black rhinoceros (40.4 ± 19.1 μmol/L) compared to white rhinoceros (29.7 ± 10.7 μmol/L). There was no association between ferritin and GGT. This study provides serum ferritin, iron, and GGT values from free-ranging black rhinoceros that can be used for as comparative target values for captive animals.
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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) | 0.000 | 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".