Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation

Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/evaluating-interactive-2d-visualization-as-a-sample-selection-strategy-for-biomedical-time-series-data-annotation

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 80
Source count: 3
Coverage: 50%

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 evaluating-interactive-2d-visualization-as-a-sample-selection-strategy-for-biomedical-time-series-data-annotation | Route /signal-canvas/evaluating-interactive-2d-visualization-as-a-sample-selection-strategy-for-biomedical-time-series-data-annotation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/evaluating-interactive-2d-visualization-as-a-sample-selection-strategy-for-biomedical-time-series-data-annotation

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: 80

Proof: Verification pending

Freshness state: computing

Source paper: Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation

PDF: https://arxiv.org/pdf/2603.26592v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:58:10.273Z

Signal Canvas receipt window

Not build-ready: Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation

/buildability/evaluating-interactive-2d-visualization-as-a-sample-selection-strategy-for-biomedical-time-series-data-annotation

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Subject: Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation

Verdict

Ignore

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

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Across all classification tasks, 2DV performed best when aggregating labels across annotators.
Implicationpartial
This is a primary finding explicitly stated in the abstract and supported by the overall results.
Verificationpartial
partial
Evidencepartial
In IMA, 2DV most effectively captured rare classes
Implicationpartial
This is a specific result mentioned in the abstract regarding the performance of 2DV in a particular domain.
Verificationpartial
partial
Evidencepartial
but also exhibited greater annotator-to-annotator label distribution variability resulting from the limited annotation budget, decreasing classification performance when models were trained on individual annotators' labels
Implicationpartial
This is a specific limitation and its consequence for the 2DV method in IMA, clearly stated in the abstract.
Verificationpartial
partial
Evidencepartial
in these cases, FAFT excelled.
Implicationpartial
This is a comparative result highlighting the strength of FAFT in a specific scenario where 2DV struggled.
Verificationpartial
partial
Evidencepartial
For SER, 2DV outperformed the other methods among expert annotators and matched their performance for non-experts in the individual-annotator setting.
Implicationpartial
This is a specific result detailing the performance of 2DV across different annotator expertise levels in SER.
Verificationpartial
partial
Evidencepartial
A failure risk analysis revealed that RND was the safest choice when annotator count or annotator expertise was uncertain, whereas 2DV had the highest risk due to its greater label distribution variability.
Implicationpartial
This is a direct conclusion from the failure risk analysis, clearly stated in the abstract.
Verificationpartial
partial
Evidencepartial
Furthermore, post-experiment interviews indicated that 2DV made the annotation task more interesting and enjoyable.
Implicationpartial
This is a qualitative finding from post-experiment interviews, indicating a benefit of the 2DV method.
Verificationpartial
partial
Evidencepartial
Overall, 2DV-based sampling appears promising for biomedical time-series data annotation, particularly when the annotation budget is not highly constrained.
Implicationpartial
This is the overarching conclusion of the study, summarizing the potential of the 2DV method.
Verificationpartial
partial

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Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation

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