Hematologic and biochemical RIs for an aged population of captive African Green monkeys (<i>Chlorocebus aethiops sabaeus</i>)
Bibliographic record
Abstract
BACKGROUND: Established RIs for geriatric African Green monkeys (Chlorocebus aethiops sabaeus) are critical for clinical differentiation of normal aging from disease-related changes in this population. OBJECTIVE: The aim of this study was to establish hematologic and serum biochemical RIs for a Caribbean captive population of geriatric (≥ 15 years of age) African Green monkeys, or Vervets. METHODS: Inclusion and exclusion criteria were defined for a cohort of 109 healthy, aged (15- to 30-year-old, median 19-year-old) Vervets. Both male (34) and female (75) monkeys were included in RI generation. Complete manual and analyzer-generated blood counts and serum biochemistry profiles were performed at Ross University School of Veterinary Medicine, West Farm, St. Kitts, West Indies. All results were evaluated using Reference Value Advisor. Isolated outliers were identified using Dixon's outlier range statistic and not included in determination of RIs for individual analytes. Reference intervals were determined using parametric and nonparametric methods depending on the distribution. Data, including mean, median, maximum, and minimum values, were tabulated. RESULTS: Of the 109 animals, 12 monkeys were excluded due to abnormal physical examination results (2 monkeys), and ≥ 2 confirmed outliers (9 monkeys), or evidence of disease based on laboratory data (one monkey). CONCLUSIONS: This study provides useful RIs for assessment of hematology and serum biochemical variables in a geriatric population of African Green monkeys in the Caribbean.
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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 | 0.001 | 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 | 0.001 | 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".