R. C. Fur distributionoftheerrortermsandanestimateofthedistributionofthewoefficient
Bibliographic record
Abstract
estimates. Each ‘trial’consists of draws of the error terms for each quarter of the forecast period and of the coefficients. Stochastic-simulation procedures are to some extent model specific, and for purposes of describing the method it is unnecessary to discuss the details of any particular procedure. The procedure that was followed for the results in this paper is discussed in Senion 11.3. Let ai, denote the variance of the forecast error for a k-quarter-ahead forecast ofvariable i from a simulation beginning in quarter I, and kt ai, denote the stochastic-simulation estimate of &.’ It is also possible to estimate by means ofstochasticsimulation the uncertainty of a model‘s forecast that is due to the uncertainty of the exogenous variables, given an assumption about the uncertainty of the exogenous variables themselves. There are two polar assumptions that can he made about the uncertainty of the exogenous variables. One is, of course. that there is no exogenous-variable uncertainty. The other is that the exogenous-variable forecasts are in some way as uncertain as the endogenous-variable forecasts.
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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".