Statistical characterization of random field parameters using frequentist and Bayesian approaches
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
Because information collected in a site investigation is limited, it is not possible to obtain actual values for the mean, standard deviation, and scale of fluctuation for a soil property of interest. The deviation between the estimated values and the actual values is called the statistical uncertainty. There are at least two schools of thought on how to model the statistical uncertainty: frequentist thought and Bayesian thought. The purpose of this paper is to discuss their philosophical difference, to show how to quantify the statistical uncertainty based on these two distinct schools of thought, and to compare their performances. To quantify the statistical uncertainty, the confidence interval will be used for the frequentist school of thought, whereas the posterior probability distribution will be used for the Bayesian school of thought. Examples will be presented to compare the performances of these two schools of thought in terms of their consistencies. The results show that, in general, the Bayesian thought performs better in terms of consistency. In particular, the Markov chain Monte Carlo method is recommended when the amount of information available is very limited.
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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) | 0.000 | 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 it