Beyond the Fold: Quantifying Split-Level Noise and the Case for Leave-One-Dataset-Out AU Evaluation

Beyond the Fold: Quantifying Split-Level Noise and the Case for Leave-One-Dataset-Out AU Evaluation | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/beyond-the-fold-quantifying-split-level-noise-and-the-case-for-leave-one-dataset-out-au-evaluation

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID beyond-the-fold-quantifying-split-level-noise-and-the-case-for-leave-one-dataset-out-au-evaluation | Route /signal-canvas/beyond-the-fold-quantifying-split-level-noise-and-the-case-for-leave-one-dataset-out-au-evaluation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/beyond-the-fold-quantifying-split-level-noise-and-the-case-for-leave-one-dataset-out-au-evaluation

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Beyond the Fold: Quantifying Split-Level Noise and the Case for Leave-One-Dataset-Out AU Evaluation

PDF: https://arxiv.org/pdf/2604.02162v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.241Z

Signal Canvas receipt window

Not build-ready: Beyond the Fold: Quantifying Split-Level Noise and the Case for Leave-One-Dataset-Out AU Evaluation

/buildability/beyond-the-fold-quantifying-split-level-noise-and-the-case-for-leave-one-dataset-out-au-evaluation

Ignoreblocked

Subject: Beyond the Fold: Quantifying Split-Level Noise and the Case for Leave-One-Dataset-Out AU Evaluation

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
repeated 3-fold subject-exclusive splits produce an empirical noise floor of $\pm 0.065$ in average F1
Implicationpartial
Directly stated in abstract with specific numeric value and dataset reference
Verificationpartial
partial
Evidencepartial
cross-validation itself introduces measurable stochastic variance
Implicationpartial
Explicitly stated as a main finding in the abstract
Verificationpartial
partial
Evidencepartial
Operating-point metrics such as F1 fluctuate more than threshold-independent measures such as AUC
Implicationpartial
Directly stated in abstract with comparison between metric types
Verificationpartial
partial
Evidencepartial
model ranking can change under different fold assignments
Implicationpartial
Directly stated in abstract as a consequence of cross-validation variance
Verificationpartial
partial
Evidencepartial
Leave-one-dataset-out cross-validation yields more stable and interpretable findings
Implicationpartial
Direct conclusion stated in abstract with rationale
Verificationpartial
partial
Evidencepartial
LODO removes partition randomness and exposes domain-level instability that is not visible under single-dataset cross-validation
Implicationpartial
Directly stated in abstract with specific comparison between protocols
Verificationpartial
partial
Evidencepartial
gains often reported in cross-fold validation may fall within protocol variance
Implicationpartial
Strongly implied conclusion based on the demonstrated variance, though not explicitly quantified
Verificationpartial
partial
Evidencepartial
with substantially larger variation for low-prevalence AUs
Implicationpartial
Directly stated in abstract with comparative language
Verificationpartial
partial

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Beyond the Fold: Quantifying Split-Level Noise and the Case for Leave-One-Dataset-Out AU Evaluation

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