Concordance Modeling With a Gold Standard for Variables From the Three-Parameter Gamma Distribution
Bibliographic record
Abstract
A way to compare two or more measurements for the same random variable can be achieved by using a negligible error reference measurement, which is called the gold standard, obtained by consolidated measurement methods. This paper presents a new methodology for comparing measurements in the presence of a gold standard with random variables from the multivariate three-parameter (shape, scale, and location) gamma distribution. The errors between gold standard measures and approximate measures have a gamma difference distribution with the same three parameters of the gamma distribution. The concordance measurements were obtained by mean of a coefficient, which measures the degree of agreement as a ratio between the variances of the gold standard and the errors. The developed methodology is illustrated with climatic data which is divided into four ranges. The measurements analyzed are rainfall forecasts of the following four national centers: Canadian Meteorological Center (CMC), European Center for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and Center for Weather Forecasting and Climate Studies (CPTEC). The forecast range was 240 hours for the West mesoregion of Paraná – Brazil, and in the October 1–March 31 period of the 2010/2011 –2015/2016 harvest years. The period was selected because it is related to soybean crop development in the region and because several crop estimation models use rainfall forecast data in this period. The methodology applied spatially indicated the center to be selected in each geographical location according to each rainfall range interval. The gamma model fit well with the data and is an alternative to the normal one for modelling rainfall, in particular to estimate concordances between rainfall forecasts and the gold standard, which are used to improve the selection of rainfall forecast centers.
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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 | 0.000 | 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 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".