Bibliographic record
Abstract
ABSTRACT Lanjar Putut Sarwoko, 2006. ONE-QUARTER FRACTION OF THE 25 DESIGN. Faculty of Mathematics and Natural Sciences, Sebelas Maret University. The 25 factorial design is a factorial design that contains 5 factors where each factor has two levels that 32 treatment combinations and 32 unit of experiments will be needed. Frequently, the whole experiment units can’t be done, so that a part of the whole treatments combinations or a part of the whole replications can be taken. The purpose of this study are to divide the treatments into four blocks for one-quarter fraction of the 25 factorial design and to analyze the statistics. To solve the problem of one-quarter fraction of the 25 factorial design, two of the defining contrasts that are the high-ordered interaction effects which weren’t significant were determined. Then we do confounding and take one of four blocks randomly, do the test of hypothesis and take a conclusion. Based on the two defining contrasts selected i.e. ABD and ACE effects, and BCDE effect as the generalized interactions, the whole treatments are classified into four blocks. As block 4 which contains the treatment combinations a, bc, abd, cd, be, ace, de, abcde is chosen, this block is tested. The hypothesis of the five main effects is tested using SSE = SSBC + SSCD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.023 |
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 source (direct Gemma or distilled Codex), 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".