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Record W4390864899

Products of the iteration of the divisor function

2023· preprint· en· W4390864899 on OpenAlexfundno aff
John Campbell

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2023
Typepreprint
Languageen
FieldMathematics
TopicAlgebraic and Geometric Analysis
Canadian institutionsnot available
FundersKillam Trusts
KeywordsDivisor (algebraic geometry)Function (biology)MathematicsDivisor functionApplied mathematicsAlgebra over a fieldPure mathematicsBiology
DOInot available

Abstract

fetched live from OpenAlex

Let $d(n)$ denote the number of divisors of $n$. Erd{\H{o}}s, in 1968, proved the remarkable result whereby there exists a positive constant $ d_{2} < \infty$ such that $$ \lim_{x \to \infty} \frac{\sum_{n \leq x} d(d(n))}{x \log \log x} = d_{2}, $$ and this was improved by Rieger in 1972. If we compare the above result to Ramanujan's formula for $ \prod_{n=1}^{x} d(n)$ and its relation to the Dirichlet formula for $\sum_{n = 1}^{x} d(n)$, this raises questions as to the behaviour of products of the form $\prod_{n=1}^{x} d(d(n))$. By the AM-GM inequality, Erd{\H{o}}s' result gives us that $$ \frac{\left( \prod_{n=1}^{x} d(d(n)) \right)^{\frac{1}{x}}}{\log \log x} $$ is bounded above by a positive, finite constant, but this does not give that the limit of the above expression exists as a positive constant, and known bounds for expressions such as $d(n)$ and $\log d(d(n))$ cannot be used in any direct way. Moreover, as we discuss, it seems that Erd{\H{o}}s' and Rieger's summation techniques cannot be altered so as to be applicable to expressions such as $\sum_{n \leq x} \log d(d(n))$ or $\prod_{n \leq x} d(d(n))$. We prove that: For all $\epsilon > 0$, the bounds $$ \log 2 - \epsilon \leq \frac{\sum_{n \leq x} \log d(d(n)) }{x \log \log \log x} \leq 1 + \epsilon $$ hold for all for sufficiently large $x$, yielding bounds for the product obtained by replacing $d(n)$ with $d(d(n))$ in Ramanujan's product.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.247
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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