<i>m</i>-embedded Subgroups and <i>p</i>-nilpotency of Finite Groups
Bibliographic record
Abstract
Abstract Let A be a subgroup of a finite group G and ∑ some subgroup series of G . Suppose that for each pair ( K , H ) such that K is a maximal subgroup of H and G i -1 ≤ K < H ≤ G i , for some i, either A ∩ H = A ∩ K or AH = AK . Then A is said to be ∑-embedded in G . And A is said to be m -embedded in G if G has a subnormal subgroup T and a {1 ≤ G }-embedded subgroup C in G such that G = AT and T ∩ A ≤ C ≤ A . In this article, some sufficient conditions for a finite group G to be p -nilpotent are given whenever all subgroups with order p k of a Sylow p -subgroup of G are m -embedded for a given positive integer k .
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".