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Paper Citation Record · LEDGER

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions

As of 8 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2506.22679.

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

pith.paper-citation-record.v1
2506.22679 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:06:05.320718Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:06:02.530416Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:06:05.772852Z

Reference resolution

32 of 32 outbound references displayed

  • verified exact1
  • verified fuzzy21
  • unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3368d40e-5550-4a2e-9cfb-097dace898b6 · outbound

This paper cites an unresolved cited work.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Unresolved cited work

Reference 1

Resolution
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Source-reported events for the cited work

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Observation 47c6269a-a8ba-472b-ac21-1e6917a3e487 · outbound

This paper cites Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions

Reference 2

Resolution
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Source-reported events for the cited work

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Observation 825ca329-bd37-4b1b-aaaa-06648824315f · outbound

This paper cites National Aeronautics and Space Administration (NASA).

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions National Aeronautics and Space Administration (NASA)

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 7c55fb9a-3497-4fb6-b81d-0f5d0c27e67a · outbound

This paper cites an unresolved cited work.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Unresolved cited work

Reference 4

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d0ab2201-24ed-44d7-9186-45a91768477b · outbound

This paper cites Fine-tuning on in-domain data improves performance.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Fine-tuning on in-domain data improves performance

Reference 5

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Source-reported events for the cited work

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Observation 34d0d2d2-5e92-4b42-9a5c-373109f260e3 · outbound

This paper cites an unresolved cited work.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Unresolved cited work

Reference 6

Resolution
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Source-reported events for the cited work

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Observation 054dbda8-8a92-41fb-a818-c453448fecbd · outbound

This paper cites Are llms robust for spoken dialogues?.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Are llms robust for spoken dialogues?

Reference 7

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 233b42e3-9126-4d21-9c5d-298d3e7afa3d · outbound

This paper cites Zero-shot spoken language understanding via large language models: A preliminary study,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Zero-shot spoken language understanding via large language models: A preliminary study,

Reference 8

Resolution
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Source-reported events for the cited work

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Observation 84ef243d-3a97-4d99-a3e9-778ed73496da · outbound

This paper cites Can ChatGPT detect intent? evaluat- ing large language models for spoken language understanding,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Can ChatGPT detect intent? evaluat- ing large language models for spoken language understanding,

Reference 9

Resolution
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Source-reported events for the cited work

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Observation 751b0907-1313-46c0-af51-6c806eccd9e0 · outbound

This paper cites Language Models are Few-Shot Learners.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Language Models are Few-Shot Learners

Reference 10

Resolution
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Source-reported events for the cited work

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Observation b69ee10f-3ca0-4c83-9b0b-1d0c6a8c2394 · outbound

This paper cites Chain-of-thought prompting elic- its reasoning in large language models,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Chain-of-thought prompting elic- its reasoning in large language models,

Reference 11

Resolution
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Source-reported events for the cited work

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Observation 25b5a211-06ac-4e64-93c5-1c53ac020e05 · outbound

This paper cites Can generative artificial intelli- gence productivity tools support workplace learning? a qualitative study on employee perceptions in a multinational corporation,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Can generative artificial intelli- gence productivity tools support workplace learning? a qualitative study on employee perceptions in a multinational corporation,

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 785594cd-6300-4c71-8afd-c925f49f5838 · outbound

This paper cites LLM-based Smart Reply (LSR): Enhancing Collaborative Performance with ChatGPT-mediated Smart Reply System.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions LLM-based Smart Reply (LSR): Enhancing Collaborative Performance with ChatGPT-mediated Smart Reply System

Reference 13

Resolution
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Source-reported events for the cited work

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Observation 6fd7507f-f248-4d48-8b09-3b897c63311e · outbound

This paper cites Conversational ai as the new employee liaison: Llm-powered chatbots in enhancing workplace collaboration and inclusion,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Conversational ai as the new employee liaison: Llm-powered chatbots in enhancing workplace collaboration and inclusion,

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation f59babff-ddc9-442f-b0ef-b3bdb741b3d5 · outbound

This paper cites Selective incivility as modern discrimination in organi- zations: Evidence and impact,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Selective incivility as modern discrimination in organi- zations: Evidence and impact,

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 92b04616-1e5d-4f56-b9cd-3273752ffcc8 · outbound

This paper cites Microaggressions, everyday dis- crimination, workplace incivilities, and other subtle slights at work: A meta-synthesis,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Microaggressions, everyday dis- crimination, workplace incivilities, and other subtle slights at work: A meta-synthesis,

Reference 16

Resolution
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Source-reported events for the cited work

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Observation 258cd884-7e04-4b1f-b694-05ef5769cc02 · outbound

This paper cites What’s that supposed to mean? capturing micro- behaviors in teams,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions What’s that supposed to mean? capturing micro- behaviors in teams,

Reference 17

Resolution
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Source-reported events for the cited work

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Observation 72111356-2dc5-4686-9768-f0081ee1dfbe · outbound

This paper cites an unresolved cited work.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Unresolved cited work

Reference 18

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Source-reported events for the cited work

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Observation 226bd455-75e3-4651-9852-0073d9431911 · outbound

This paper cites Automated detection of racial microaggressions using machine learning,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Automated detection of racial microaggressions using machine learning,

Reference 19

Resolution
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Source-reported events for the cited work

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Observation 197fcce8-4b2b-48f8-baf7-e1c14172575f · outbound

This paper cites Finding mi- croaggressions in the wild: A case for locating elusive phenom- ena in social media posts,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Finding mi- croaggressions in the wild: A case for locating elusive phenom- ena in social media posts,

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 13ac2896-d5d0-4c30-ae69-d173d637255a · outbound

This paper cites Leveraging bias in pre- trained word embeddings for unsupervised microaggression de- tection,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Leveraging bias in pre- trained word embeddings for unsupervised microaggression de- tection,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 7933776a-bb73-472c-aac7-0cf34361fe0d · outbound

This paper cites Overview of machine learning algorithms for detect- ing microaggression in written text,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Overview of machine learning algorithms for detect- ing microaggression in written text,

Reference 22

Resolution
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Source-reported events for the cited work

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Observation 04785774-ac7a-407e-bb12-221e6c70f268 · outbound

This paper cites Towards identification of microaggressions in real-life and scripted conversations, using context-aware machine learning techniques,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Towards identification of microaggressions in real-life and scripted conversations, using context-aware machine learning techniques,

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation b033f9bb-5c5d-45b7-bd2c-814dae26ce57 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 24

Resolution
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Source-reported events for the cited work

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Observation 2a4196e5-0a42-4eba-b12c-faff25b0c39e · outbound

This paper cites Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,

Reference 25

Resolution
verified fuzzy
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Source-reported events for the cited work

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This paper cites Twitter-roberta-base for sentiment analysis - up- dated (2022),.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Twitter-roberta-base for sentiment analysis - up- dated (2022),

Reference 27

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 2cccb562-2711-4a9d-a42a-c98585cded95 · outbound

This paper cites Distilbert base uncased finetuned sst-2,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Distilbert base uncased finetuned sst-2,

Reference 28

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation f348cbb2-e5f1-44fa-9d92-be49f41e6219 · outbound

This paper cites Pegasus: Pre-training with extracted gap-sentences for abstractive summarization,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Pegasus: Pre-training with extracted gap-sentences for abstractive summarization,

Reference 29

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 452955ac-d86d-4042-90e7-10ae54aa0448 · outbound

This paper cites PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

Reference 30

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Source-reported events for the cited work

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Observation c7323a0a-3598-49fd-bcf2-434f4c9dc53c · outbound

This paper cites A survey on in-context learning,.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions A survey on in-context learning,

Reference 31

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verified fuzzy
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Source-reported events for the cited work

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Observation 26a3cbe6-38e9-461f-9905-ff2854211263 · outbound

This paper cites The Llama 3 Herd of Models.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions The Llama 3 Herd of Models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T22:06:05.320718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4085e5b1-c5fc-4f24-8ebd-677af39be5b1 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-06T22:06:04.712196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

Observation 47c6269a-a8ba-472b-ac21-1e6917a3e487 · inbound

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions cites this paper.

Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions Assessing the feasibility of Large Language Models for detecting micro-behaviors in team interactions during space missions

Reference 2

Resolution
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local_arxiv, observed 2026-08-06T22:06:05.829656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T22:06:02.530416Z digest=sha256:3d7eec18bb099996dd0f49a741ac5fe92aff65c8e646a4a93a47cd6a9ed94f62