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AI is dramatically lowering the cost of reading papers, writing code, and running experiments. But as research output explodes, telling which results can be trusted keeps getting harder. CiteArk connects the claims in scientific papers with their experimental conditions, execution process, result evidence, and verification records, so researchers and agents can verify claims, reuse experimental results, and build follow-up research on them instead of repeating work. We hope these trustworthy research records will grow into a scientific foundation that researchers, labs, and agents can all build on together.

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The authors empirically report that normalizing Reddit features to 'zero mean and univariate' is crucial for SGC. · CiteArk

Simplifying Graph Convolutional Networks

Not assessedPlan blockedFindingclaim-reddit-normalization

The authors empirically report that normalizing Reddit features to 'zero mean and univariate' is crucial for SGC.

Source: paper:PDF p.13, Node Classification

Reported and observed measurements

No structured measurement is attached to this Claim.

Assessments (0)

No immutable Assessment has been published for this Claim yet.

Runs (0)

No execution has been linked to this Claim yet.

Research Plan identity

Claim version: sha256:35b8851a73a49a8a37e716a4c65af28cde2ffd36a1a0d2f471e04346d6dc67a5
Planned experiments: 1
Evidence objects: 0

At least one source-grounded package is cataloged. Operational uncertainties and source omissions are disclosed in its objective; observed evidence is still required.

Machine-readable Claim

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