Standing waves of the quintic NLS equation on the tadpole graph
Bibliographic record
Abstract
Abstract The tadpole graph consists of a circle and a half-line attached at a vertex. We analyze standing waves of the nonlinear Schrödinger equation with quintic power nonlinearity equipped with the Neumann–Kirchhoff boundary conditions at the vertex. The profile of the standing wave with the frequency $$\omega \in (-\infty ,0)$$ ω ∈ ( - ∞ , 0 ) is characterized as a global minimizer of the quadratic part of energy constrained to the unit sphere in $$L^6$$ L 6 . The set of standing waves includes the set of ground states, which are the global minimizers of the energy at constant mass ( $$L^2$$ L 2 -norm), but it is actually wider. While ground states exist only for a certain interval of masses, the standing waves exist for every $$\omega \in (-\infty ,0)$$ ω ∈ ( - ∞ , 0 ) and correspond to a bigger interval of masses. It is proven that there exist critical frequencies $$\omega _1$$ ω 1 and $$\omega _0$$ ω 0 with $$-\infty< \omega _1< \omega _0 < 0$$ - ∞ < ω 1 < ω 0 < 0 such that the standing waves are the ground state for $$\omega \in [\omega _0,0)$$ ω ∈ [ ω 0 , 0 ) , local constrained minima of the energy for $$\omega \in (\omega _1,\omega _0)$$ ω ∈ ( ω 1 , ω 0 ) and saddle points of the energy at constant mass for $$\omega \in (-\infty ,\omega _1)$$ ω ∈ ( - ∞ , ω 1 ) . Proofs make use of the variational methods and the analytical theory for differential equations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".