Mean curvature in manifolds with Ricci curvature bounded from below
Bibliographic record
Abstract
Let M be a compact Riemannian manifold of nonnegative Ricci curvature and \Sigma a compact embedded 2-sided minimal hypersurface in M . It is proved that there is a dichotomy: If \Sigma does not separate M then \Sigma is totally geodesic and M\setminus\Sigma is isometric to the Riemannian product \Sigma\times(a,b) , and if \Sigma separates M then the map i_*:\pi_1(\Sigma)\rightarrow \pi_1(M) induced by inclusion is surjective. This surjectivity is also proved for a compact 2-sided hypersurface with mean curvature H\geq(n-1)\sqrt{k} in a manifold of Ricci curvature Ric _M\geq-(n-1)k , k>0 , and for a free boundary minimal hypersurface in an n -dimensional manifold of nonnegative Ricci curvature with nonempty strictly convex boundary. As an application it is shown that a compact (n-1) -dimensional manifold N with the number of generators of \pi_1(N) < n-1 cannot be minimally embedded in the flat torus T^{n} .
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".