AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics

AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/astroconcepts-a-large-scale-multi-label-classification-corpus-for-astrophysics

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
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 astroconcepts-a-large-scale-multi-label-classification-corpus-for-astrophysics | Route /signal-canvas/astroconcepts-a-large-scale-multi-label-classification-corpus-for-astrophysics

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/astroconcepts-a-large-scale-multi-label-classification-corpus-for-astrophysics

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "astroconcepts-a-large-scale-multi-label-classification-corpus-for-astrophysics",
    "query_text": "Summarize AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics",
  "normalized_query": "2604.02156",
  "route": "/signal-canvas/astroconcepts-a-large-scale-multi-label-classification-corpus-for-astrophysics",
  "paper_ref": "astroconcepts-a-large-scale-multi-label-classification-corpus-for-astrophysics",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics

PDF: https://arxiv.org/pdf/2604.02156v1

Source count: Pending verification

Coverage: 33%

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

Signal Canvas receipt window

Not build-ready: AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics

/buildability/astroconcepts-a-large-scale-multi-label-classification-corpus-for-astrophysics

Ignoreblocked

Subject: AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics

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
We introduce AstroConcepts, a corpus of English abstracts from 21,702 published astrophysics papers, labeled with 2,367 concepts from the Unified Astronomy Thesaurus.
Implicationpartial
Directly stated in the abstract with specific numbers
Verificationpartial
partial
Evidencepartial
The corpus exhibits severe label imbalance, with 76% of concepts having fewer than 50 training examples.
Implicationpartial
Directly stated in the abstract with specific percentage
Verificationpartial
partial
Evidencepartial
First, vocabulary-constrained LLMs achieve competitive performance relative to domain-adapted models in astrophysics classification, suggesting a potential for parameter-efficient approaches.
Implicationpartial
Directly stated as a key finding in the abstract
Verificationpartial
partial
Evidencepartial
Second, domain adaptation yields relatively larger improvements for rare, specialized terminology, although absolute performance remains limited across all methods.
Implicationpartial
Directly stated in the abstract but qualified with 'relatively larger improvements'
Verificationpartial
partial
Evidencepartial
although absolute performance remains limited across all methods.
Implicationpartial
Directly stated in the abstract as a limitation of current methods
Verificationpartial
partial
Evidencepartial
Third, we propose frequency-stratified evaluation to reveal performance patterns that are hidden by aggregate scores, thereby making robustness assessment central to scientific multi-label evaluation.
Implicationpartial
Directly stated as a proposed method and key finding in the abstract
Verificationpartial
partial
Evidencepartial
Existing scientific corpora lack comprehensive controlled vocabularies, focusing instead on broad categories and limiting systematic study of extreme imbalance.
Implicationpartial
Directly stated in the abstract but presented as background/context rather than a primary finding
Verificationpartial
partial
Evidencepartial
By releasing this resource, we enable systematic study of extreme class imbalance in scientific domains and establish strong baselines across traditional, neural, and vocabulary-constrained LLM methods.
Implicationpartial
Directly stated as a purpose of the resource in the abstract
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics

Use Signal Canvas as the narrative proof surface

Use this Signal Canvas via API or MCP

Signal Canvas proof surface