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Paper Citation Record · LEDGER

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2509.19817.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.19817 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:22:18.839404Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:22:16.346725Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved34
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 01416b95-20cd-45e2-b428-7ea445bf9c33 · outbound

This paper cites MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition

Reference 1

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source=pdf_text observed=2026-08-04T15:22:16.346725Z digest=sha256:2a1e283b40486071a6717a5aab05046a18a6197473d5a6191cd61d87d91fd55a

Observation 649911c2-2ac0-4b9f-bea2-32b0920535c6 · outbound

This paper cites an unresolved cited work.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-04T15:22:16.374793Z digest=sha256:25691b2f00f462de39cb41125eaa5e9839a321b6dcfb74decc4b57196be54c3f

Observation c1b527c2-278d-41df-b688-e1cf3bb93fa7 · outbound

This paper cites an unresolved cited work.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-04T15:22:16.395436Z digest=sha256:98cc7ea6036d066133a6834ac7ea112c0d08f99b1f247bb9f81f87b7c2972f73

Observation 8fa8267e-00f7-46d3-91eb-1db53ad5b906 · outbound

This paper cites an unresolved cited work.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-04T15:22:16.413873Z digest=sha256:3dac0f60a1113548eb957f2d6bc9d00bbdcf4eb6836dafa120951ebe74e56500

Observation 3beceeed-091e-47f7-8c22-36fe5b4c48c9 · outbound

This paper cites Data Acquisition We collected speech from a full-duplex healthcare assistant during internal testing (beta version).

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Data Acquisition We collected speech from a full-duplex healthcare assistant during internal testing (beta version)

Reference 5

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source=pdf_text observed=2026-08-04T15:22:16.421986Z digest=sha256:f6fe8a64d1ddfd3355f4e1a2afd1070b1b8268b31f5188e2c4ac7cfc131f41b3

Observation 39c74ce4-1db1-445c-9c16-f10957b32bc8 · outbound

This paper cites Experimental Setups We fine-tuned Whisper-small [14] end-to-end on the Chinese training split for automatic speech recognition in healthcare dialogue.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Experimental Setups We fine-tuned Whisper-small [14] end-to-end on the Chinese training split for automatic speech recognition in healthcare dialogue

Reference 6

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source=pdf_text observed=2026-08-04T15:22:16.440505Z digest=sha256:36736dbe2c267a0d3717171d712f4694ddb7e499ca728f551a1c9483a6288129

Observation 2139dc29-1c71-4c84-b24b-95e52315d078 · outbound

This paper cites Benchmark Description MMedFD is a benchmark for Chinese healthcare spoken di- alogue constructed from live user–agent interactions under full-duplex conditions.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Benchmark Description MMedFD is a benchmark for Chinese healthcare spoken di- alogue constructed from live user–agent interactions under full-duplex conditions

Reference 7

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source=pdf_text observed=2026-08-04T15:22:16.459548Z digest=sha256:6153585a718b2934bffc10a5f720085df058dd2b974402095e54f7edbbb9536b

Observation e8541f26-1d29-4030-a3b6-61de438dca0c · outbound

This paper cites LLM-judged results for healthcare queries using PairEval and G-Eval with a consistent GPT-5 judge.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition LLM-judged results for healthcare queries using PairEval and G-Eval with a consistent GPT-5 judge

Reference 8

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source=pdf_text observed=2026-08-04T15:22:16.479683Z digest=sha256:dbdc6ce32b8fcddf5fa55612d0b870a3e5ffd8557d7343bbcba5938123fce5f2

Observation e48a6ac4-9485-4a54-a45d-823edc0ccaf4 · outbound

This paper cites P0051278, Jung Sun Yoo) and by the Research Grants Council of the Hong Kong Special Administrative Region, China (General Re- search Fund, Project No.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition P0051278, Jung Sun Yoo) and by the Research Grants Council of the Hong Kong Special Administrative Region, China (General Re- search Fund, Project No

Reference 9

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source=pdf_text observed=2026-08-04T15:22:16.498586Z digest=sha256:2a7b3a107faa24fd4be39afaad1ff63e97692b0dfda94ff780f895d5b365d45c

Observation 1f285f29-c215-4f79-a973-9a3dfb789d4b · outbound

This paper cites The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models

Reference 10

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source=pdf_text observed=2026-08-04T15:22:16.516308Z digest=sha256:b1466638115ef2a994852ae012be0af2b893a315a48bd55ec3151722fbcd07ef

Observation 5e74c4ea-0f3a-4e9d-ba0f-e1a8147d0af9 · outbound

This paper cites Medical dialogue system: A survey of cat- egories, methods, evaluation and challenges,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Medical dialogue system: A survey of cat- egories, methods, evaluation and challenges,

Reference 11

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source=pdf_text observed=2026-08-04T15:22:16.533560Z digest=sha256:71cbdaca5d25fcefe0a76b370eec5322c02816671ba284c82af6f9fd6f43c238

Observation 0b5aef1e-162a-4672-9479-ac6746e7ece1 · outbound

This paper cites Multimed: Multilingual medi- cal speech recognition via attention encoder decoder,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Multimed: Multilingual medi- cal speech recognition via attention encoder decoder,

Reference 12

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source=pdf_text observed=2026-08-04T15:22:16.542090Z digest=sha256:f139e179eebd1e06e8c2e4fd804a68282b64a61efc764d05ddca3b7b1814f3f0

Observation e4f1f9e1-5cb7-43aa-8714-bfe04932987f · outbound

This paper cites The AI doctor is in: A survey of task-oriented dialogue systems for healthcare appli- cations,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition The AI doctor is in: A survey of task-oriented dialogue systems for healthcare appli- cations,

Reference 13

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source=pdf_text observed=2026-08-04T15:22:16.554319Z digest=sha256:352ded48489356016b3c63d88cc574e639c6f305dd0750b1d83dcf0ddf1819ce

Observation 5f00b907-f0b3-47d6-bca7-738c349cf809 · outbound

This paper cites A full-duplex speech dialogue scheme based on large language model,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition A full-duplex speech dialogue scheme based on large language model,

Reference 14

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source=pdf_text observed=2026-08-04T15:22:16.618816Z digest=sha256:b803c83bef72e1eb36e6cbca2dea5ae85c917f94de501d3c6bd44b6b5b27f868

Observation 1d54ba5c-a68b-46f1-ada7-435d97fdf422 · outbound

This paper cites Primock57: A dataset of primary care mock consultations,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Primock57: A dataset of primary care mock consultations,

Reference 15

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source=pdf_text observed=2026-08-04T15:22:16.779249Z digest=sha256:aeabaae1eee8cd0ca7bb3cfbf2ba838ebc8dc41c55154b61b0e6a74c59d65db3

Observation 3f14d887-cffa-4a98-bf23-b5228285741a · outbound

This paper cites Mtalk-bench: Evaluating speech-to- speech models in multi-turn dialogues via arena-style and rubrics protocols,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Mtalk-bench: Evaluating speech-to- speech models in multi-turn dialogues via arena-style and rubrics protocols,

Reference 16

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source=pdf_text observed=2026-08-04T15:22:16.839554Z digest=sha256:1f54387703d6201b0d714b25a56b29fa2965c1804a20f68043d1d72341711a76

Observation ab8d67bf-2161-42f8-9cde-8ff01c3e3e80 · outbound

This paper cites an unresolved cited work.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-04T15:22:16.923506Z digest=sha256:554aa5a3b72da7fe268cab5de54ea338eadd756af01ff49ddcdc9a4800f6ad43

Observation 894c153e-c88d-43be-87d7-9d6aa087ae33 · outbound

This paper cites KWS15 keyword search evaluation plan,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition KWS15 keyword search evaluation plan,

Reference 18

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source=pdf_text observed=2026-08-04T15:22:17.094461Z digest=sha256:d765109621e67f52dc11441988213928af808f382d8dca477a5e6e0bd73828d5

Observation 34bedba4-41ea-4e66-8ea1-120ad1f891c4 · outbound

This paper cites The bigscience roots corpus: A 1.6tb composite multilingual dataset,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition The bigscience roots corpus: A 1.6tb composite multilingual dataset,

Reference 19

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source=pdf_text observed=2026-08-04T15:22:17.169949Z digest=sha256:4abb0406f36e1f193201c0ab01af5342856bba70350825f4e1029ad4a8723a8e

Observation 26840883-4ca7-46d8-b1ff-c12214ed482a · outbound

This paper cites Silero vad: Pre-trained enterprise- grade voice activity detector,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Silero vad: Pre-trained enterprise- grade voice activity detector,

Reference 20

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source=pdf_text observed=2026-08-04T15:22:17.285592Z digest=sha256:bc999d191f7a7cefa819d5a9524000e4ba62f03a2857090c50bb8eb495596bbf

Observation 0a4a3281-f989-4785-8070-a5c5ff17c8f6 · outbound

This paper cites pyannote.audio: neural building blocks for speaker diarization.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition pyannote.audio: neural building blocks for speaker diarization

Reference 21

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source=pdf_text observed=2026-08-04T15:22:17.337394Z digest=sha256:adcf6bd61d4f131156b7f928e18857b82fe8abaccabb959c988fa8cf9460877d

Observation 33cd10b0-5940-4a70-b83d-30007e92a6e1 · outbound

This paper cites Openasr21 challenge evaluation plan,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Openasr21 challenge evaluation plan,

Reference 22

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source=pdf_text observed=2026-08-04T15:22:17.461986Z digest=sha256:abca52f2083dde8a8cbbd963d08c58178675bc71ca98c3bf3aa649cf63aaabe7

Observation bef2036b-edbb-46f9-9934-367d5a43e1f6 · outbound

This paper cites Robust speech recognition via large-scale weak supervision,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Robust speech recognition via large-scale weak supervision,

Reference 23

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source=pdf_text observed=2026-08-04T15:22:17.565485Z digest=sha256:837b4898483f7129229ad8f835af5614591fed63bdcc24e1fda5d8d94bea288a

Observation 6b0898ae-af83-4fcb-900a-f545ad8db60c · outbound

This paper cites Paireval: Open-domain dialogue evaluation with pairwise comparison,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Paireval: Open-domain dialogue evaluation with pairwise comparison,

Reference 24

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source=pdf_text observed=2026-08-04T15:22:17.648500Z digest=sha256:08bd2939576cb83aa0a81d2789b089d8596ffcd5ceacd9c174ac28f1f2e6da04

Observation 6acfa2f3-f6d1-4e1f-8ed6-b6f0cd6b5916 · outbound

This paper cites G-eval: NLG evaluation using GPT- 4 with better human alignment,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition G-eval: NLG evaluation using GPT- 4 with better human alignment,

Reference 25

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source=pdf_text observed=2026-08-04T15:22:17.821686Z digest=sha256:e1124ee3b4e4feea3098231ca9f2304ee9ae2bdf69a678f93fc20a5f83bfd6d5

Observation 73297133-b368-4a9d-a926-db0b443bb430 · outbound

This paper cites Vietmed: A dataset and benchmark for automatic speech recognition of vietnamese in the medical domain,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Vietmed: A dataset and benchmark for automatic speech recognition of vietnamese in the medical domain,

Reference 26

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source=pdf_text observed=2026-08-04T15:22:17.975254Z digest=sha256:f8cf2975725cfaa3973be484bb4ebbbb01c3a0ef7c7653119c1091e21d23c643

Observation 7d2f68ec-bbee-4fc7-9ba9-4f76d115b94b · outbound

This paper cites A dataset of simulated patient- physician medical interviews with a focus on respiratory cases,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition A dataset of simulated patient- physician medical interviews with a focus on respiratory cases,

Reference 27

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source=pdf_text observed=2026-08-04T15:22:18.044820Z digest=sha256:bcaee9c76480b9f49688c775df0ad8d4425fdfe5f588d0319421c4cc661eed7d

Observation f35f820f-5152-4e34-a199-613f2e0ef8ce · outbound

This paper cites mymedicon: End-to-end burmese automatic speech recognition for medical conversa- tions,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition mymedicon: End-to-end burmese automatic speech recognition for medical conversa- tions,

Reference 28

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source=pdf_text observed=2026-08-04T15:22:18.164460Z digest=sha256:6f16c90be95399ab442e3a2c4041946aaca032e7e5bf411a5451a0d664a2dd91

Observation aeddf85a-e2c2-44ba-b170-ec2c63c8b7d7 · outbound

This paper cites Afrispeech-200: Pan-african ac- cented speech dataset for clinical and general domain asr,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Afrispeech-200: Pan-african ac- cented speech dataset for clinical and general domain asr,

Reference 29

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source=pdf_text observed=2026-08-04T15:22:18.271000Z digest=sha256:c37ac46f51b095dc033f5079a410b924cf471349452b64983c2027527f9b3578

Observation 2019cf75-acdf-4c93-99a1-332bdb30537d · outbound

This paper cites Spokenwoz: A large-scale speech-text benchmark for spoken task-oriented dia- logue agents,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Spokenwoz: A large-scale speech-text benchmark for spoken task-oriented dia- logue agents,

Reference 30

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source=pdf_text observed=2026-08-04T15:22:18.335130Z digest=sha256:f7067f9921b4c64a18d1ae281c795354a47b1a938ca4886c3d161260f01884d2

Observation fec3f24b-48cb-4065-a351-4234d4267559 · outbound

This paper cites V oxdialogue: Can spoken dialogue systems understand information beyond words?,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition V oxdialogue: Can spoken dialogue systems understand information beyond words?,

Reference 31

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source=pdf_text observed=2026-08-04T15:22:18.435260Z digest=sha256:ee8f642bfdeb7094a05d02782bc7a18d3146d8a990e095ed57106c5a85209c4e

Observation 1d1d5f7f-eb21-4e7e-a131-82ebec3e0d79 · outbound

This paper cites Gpt-5 system card,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Gpt-5 system card,

Reference 32

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source=pdf_text observed=2026-08-04T15:22:18.536910Z digest=sha256:166613fb95474aa6346f76b6dc9b324444e6b8626b6f04328faf6e6f56331824

Observation 79371a43-b042-462f-ac39-a0d86e6f3693 · outbound

This paper cites Claude opus 4.1 system card addendum,.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Claude opus 4.1 system card addendum,

Reference 33

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source=pdf_text observed=2026-08-04T15:22:18.612090Z digest=sha256:ef59975959e5e90e37f72c950facd5b7b9aa47edeaffca067337ba1fc60655c5

Observation 8efe0fc2-edff-4212-bb67-07352adbcf07 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 34

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source=pdf_text observed=2026-08-04T15:22:18.751402Z digest=sha256:1d1778434d4f41e7fb5d4343aa4f0522e90c9062b195ba64468b5e41bab7aa86

Observation 7b4e32d7-8939-4d87-8f45-d77b807548f1 · outbound

This paper cites Qwen3 Technical Report.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Qwen3 Technical Report

Reference 35

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source=pdf_text observed=2026-08-04T15:22:18.839404Z digest=sha256:1038301839f350d67b0c7bddecb81cb5679dfbb78a57630437693a8c441889bb

Pith citing papers

Observation 01416b95-20cd-45e2-b428-7ea445bf9c33 · inbound

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition cites this paper.

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition

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