Bibliographic record
Abstract
Abstract We extend the Matomäki–Radziwiłł theorem to a large collection of unbounded multiplicative functions that are uniformly bounded, but not necessarily bounded by 1, on the primes. Our result allows us to estimate averages of such a function f in typical intervals of length $$h(\log X)^c$$ h ( log X ) c , with $$h = h(X) \rightarrow \infty $$ h = h ( X ) → ∞ and where $$c = c_f \ge 0$$ c = c f ≥ 0 is determined by the distribution of $$\{|f(p) |\}_p$$ { | f ( p ) | } p in an explicit way. We give three applications. First, we show that the classical Rankin–Selberg-type asymptotic formula for partial sums of $$|\lambda _f(n) |^2$$ | λ f ( n ) | 2 , where $$\{\lambda _f(n)\}_n$$ { λ f ( n ) } n is the sequence of normalized Fourier coefficients of a primitive non-CM holomorphic cusp form, persists in typical short intervals of length $$h\log X$$ h log X , if $$h = h(X) \rightarrow \infty $$ h = h ( X ) → ∞ . We also generalize this result to sequences $$\{|\lambda _{\pi }(n) |^2\}_n$$ { | λ π ( n ) | 2 } n , where $$\lambda _{\pi }(n)$$ λ π ( n ) is the nth coefficient of the standard L-function of an automorphic representation $$\pi $$ π with unitary central character for $$GL_m$$ G L m , $$m \ge 2$$ m ≥ 2 , provided $$\pi $$ π satisfies the generalized Ramanujan conjecture. Second, using recent developments in the theory of automorphic forms we estimate the variance of averages of all positive real moments $$\{|\lambda _f(n) |^{\alpha }\}_n$$ { | λ f
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".