Improved Experimental Design and Analysis for Long‐Term Experiments
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses inadequacies in the way most long‐term experiments (LTEs) are conducted and analyzed. The standard design under which LTEs are usually conducted involves a fixed start , establishing all plots in the study in the same year. This design is shown to be inadequate for the purpose of testing and estimating the time × treatment (TRT) interaction, which is generally the primary interest in a LTE. This inadequacy occurs because the repeated measures taken on every plot are all influenced simultaneously by the same random environmental conditions, the effects of which are confounded with the fixed effects of interest. No statistical analysis can completely separate the fixed effects from the random nuisance effects, although added assumptions about the shape of trends across time or covariates to describe the random effects can sometimes be helpful. An alternative experimental design, the staggered‐start design, has been used to alleviate this confounding by establishing plots from different blocks in successive years, but proper analysis of this design has not been presented. A correct analysis of the staggered‐start design is determined and presented. The analysis is applied to hypothetical data from a staggered‐start design whose true means are known, and it is shown to do a much better job of estimating these means than any methods applied to data from the standard design. A staggered start should be considered instead of a fixed start for all future LTEs.
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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 | 0.001 | 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 | 0.001 |
| 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