Parametric Interval Estimation of the Geeta Distribution
Bibliographic record
Abstract
It is well known that the sample mean is the estimator of a population mean in mathematical statistics from a given population of interest as a point estimator which assume a single number that is obtained by taking a random sample of a specified size from the entire population, depending on whether the population mean and variance is known or unknown. In the interval estimation, the sample mean is accompanied with a plus or a minus margin of an error that is assumed that the estimator is contained within the range of values with certain degree of confidence. This paper investigated and obtained the interval estimators of the unknown constants of Geeta distribution model through the construction of confidence interval using; the pivotal quantity method, the shortest-length confidence interval, unbiased confidence interval estimators, Bayesian confidence interval estimators and statistical method. Geeta distribution is a new discrete random variable distribution defined over all the positive integers, with two unknown parameters. The properties and characteristics of the Geeta distribution model were discussed and reviewed that is, the existence of the mean, variance, moment generating function and that the sum of all probabilities is unity. These are common properties of any given probability density function.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".