RANCANGAN FAKTORIAL 25 DENGAN SEPEREMPAT ULANGAN
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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