Evidence Receipt. Related Resources.
Evidence Receipt. Related Resources.
Compared to this week’s papers
Verification pending
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Route this paper proof surface into REST, MCP, or developer workflows while preserving the same evidence receipt and related-resource context.
Page Freshness
Canonical route: /signal-canvas/uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid
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 uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid | Route /signal-canvas/uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evidMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid",
"query_text": "Summarize Uncertainty-Aware Wildfire Smoke Density Classification from Satellite Imagery via CBAM-Augmented EfficientNet with Evidential Deep Learning"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "Uncertainty-Aware Wildfire Smoke Density Classification from Satellite Imagery via CBAM-Augmented EfficientNet with Evidential Deep Learning",
"normalized_query": "2605.15894",
"route": "/signal-canvas/uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid",
"paper_ref": "uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Claims: 0
References: Pending verification
Proof: Verification pending
Freshness state: computing
PDF: https://arxiv.org/pdf/2605.15894v1
Source count: 3
Coverage: 50%
Last proof check: 2026-05-18T20:28:43.437Z
Signal Canvas receipt window
/buildability/uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid
Subject: Uncertainty-Aware Wildfire Smoke Density Classification from Satellite Imagery via CBAM-Augmented EfficientNet with Evidential Deep Learning
Verdict
Preparing verified analysis
Dimensions overall score 7.0
No public code linked for this paper yet.
CLAIM MAP
No public claim map is available for this paper yet.
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
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Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid
Paper ref
uncertainty-aware-wildfire-smoke-density-classification-from-satellite-imagery-via-cbam-augmented-efficientnet-with-evid
arXiv id
2605.15894
Generated at
2026-05-18T20:28:43.437Z
Evidence freshness
stale
Last verification
2026-05-18T20:28:43.437Z
Sources
3
References
0
Coverage
50%
Lineage hash
58fb9391343e0d32e117bd94e2aa10762025cb457d05e79f880a0825744e9eb4
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Pending verification refs / 3 sources / Verification pending
repo_url
references