A rational map with infinitely many points of distinct arithmetic\n degrees
Bibliographic record
Abstract
Let $f \\colon X \\dashrightarrow X$ be a dominant rational self-map of a\nsmooth projective variety defined over $\\overline{\\mathbb Q}$. For each point\n$P\\in X(\\overline{\\mathbb Q})$ whose forward $f$-orbit is well-defined,\nSilverman introduced the arithmetic degree $\\alpha_f(P)$, which measures the\ngrowth rate of the heights of the points $f^n(P)$. Kawaguchi and Silverman\nconjectured that $\\alpha_f(P)$ is well-defined and that, as $P$ varies, the set\nof values obtained by $\\alpha_f(P)$ is finite. Based on constructions of\nBedford--Kim and McMullen, we give a counterexample to this conjecture when\n$X=\\mathbb P^4$.\n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".