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Record W3092794119 · doi:10.48550/arxiv.2010.09043

Berkovich curves and Schottky uniformization

2020· preprint· en· W3092794119 on OpenAlexaff
Jérôme Poineau, Danièle Turchetti

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldComputer Science
TopicTopological and Geometric Data Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsUniformization (probability theory)Computer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

This text is an exposition of non-Archimedean curves and Schottky\nuniformization from the point of view of Berkovich geometry. It consists of two\nparts, the first one of an introductory nature, and the second one more\nadvanced. The first part is meant to be an introduction to the theory of\nBerkovich spaces focused on the case of the affine line. We define the\nBerkovich affine line and present its main properties, with many details:\nclassification of points, path-connectedness, metric structure, variation of\nrational functions, etc. Contrary to many other introductory texts, we do not\nassume that the base field is algebraically closed. The second part is devoted\nto the theory of Mumford curves and Schottky uniformization. We start by\nbriefly reviewing the theory of Berkovich curves, then introduce Mumford curves\nin a purely analytic way (without using formal geometry). We define Schottky\ngroups acting on the Berkovich projective line, highlighting how geometry and\ngroup theory come together to prove that the quotient by the action of a\nSchottky group is an analytic Mumford curve. Finally, we present an analytic\nproof of Schottky uniformization, showing that any analytic Mumford curves can\nbe described as a quotient of this kind. The guiding principle of our\nexposition is to stress notions and fully prove results in the theory of\nnon-Archimedean curves that, to our knowledge, are not fully treated in other\ntexts.\n

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.003
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 head0.083
GPT teacher head0.183
Teacher spread0.100 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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