Essential Dimension in Mixed Characteristic
Bibliographic record
Abstract
Let G be a finite group, and let R be a discrete valuation ring with residue field k and fraction field K . We say that G is weakly tame at a prime p if it has no non-trivial normal p -subgroups. By convention, every finite group is weakly tame at 0 . We show that if G is weakly tame at char(k), then \operatorname{ed}_K(G) \geq \operatorname{ed}_k(G) . Here \operatorname{ed}_F(G) denotes the essential dimension of G over the field F . We also prove a more general statement of this type, for a class of étale gerbes \mathcal{X} over R . As a corollary, we show that if G is weakly tame at p , then \operatorname{ed}_{L} G \geq \operatorname{ed}_{k} G for any field L of characteristic 0 and any field k of characteristic p , provided that k contains \overline {\mathbb{F}}_{p} . We also show that a conjecture of A. Ledet asserting that \operatorname{ed}_k \mathbb{Z}/p^n \mathbb{Z}) = n for a field k of characteristic p>0 implies that \operatorname{ed}_{\mathbb{C}}(G) \geq n for any finite group G which is weakly tame at p and contains an element of order p^n . To the best of our knowledge, an unconditional proof of the last inequality is out of the reach of all presently known techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".