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A note on the generalization of the Kodaira embedding theorem

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Chao Li, Xi Zhang, QiZhi Zhao

Differential Geometry · Submitted 26 Sep 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this paper, by using analytical methods we obtain a generalization of the famous Kodaira embedding theorem.

how it is stated · asserted 1.00 read the record →

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arXiv ID1909.12029 (version 1)
Pith ID3YRKDCDH, Pith's permanent record of this paper and its review
CitationChao Li, QiZhi Zhao, Xi Zhang. "A note on the generalization of the Kodaira embedding theorem." arXiv:1909.12029 (2019). https://pith.science/paper/3YRKDCDH
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 1,580 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.04 · human participants 0.02 · controlled comparison 0.01 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.17 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper, by using analytical methods we obtain a generalization of the famous Kodaira embedding theorem.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper, by using analytical methods we obtain a generalization of the famous Kodaira embedding theorem. contribution 0.93 0.95 0.90 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 1.00 · evidence from proof or derivation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper, by using analytical methods we obtain a generalization of the famous Kodaira embedding theorem.

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1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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