Predicting Blood Type: Assessing Model Performance with ROC Analysis | Paper Citation Record

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This paper cites A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos.

Predicting Blood Type: Assessing Model Performance with ROC Analysis A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos

Reference 1

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This paper cites A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes.

Predicting Blood Type: Assessing Model Performance with ROC Analysis A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes

Reference 2

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This paper cites A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer.

Predicting Blood Type: Assessing Model Performance with ROC Analysis A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer

Reference 3

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This paper cites Advanced ensemble machine learning techniques for optimizing diabetes mellitus prognostication: A detailed examination of hospital data.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Advanced ensemble machine learning techniques for optimizing diabetes mellitus prognostication: A detailed examination of hospital data

Reference 4

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This paper cites Advanced Landslide Detection Using Machine Learning and Remote Sensing Data.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Advanced Landslide Detection Using Machine Learning and Remote Sensing Data

Reference 5

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This paper cites Advanced Machine Learning Models for Real -Time Drone and Bird Differentiation in Aerial Surveillance Systems.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Advanced Machine Learning Models for Real -Time Drone and Bird Differentiation in Aerial Surveillance Systems

Reference 6

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This paper cites Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID -19 Detection, Lung Infection, and Tumor Segmentation.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID -19 Detection, Lung Infection, and Tumor Segmentation

Reference 7

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This paper cites AI in the Sky: Developing Real -Time UAV Recognition Systems to Enhance Military Security.

Predicting Blood Type: Assessing Model Performance with ROC Analysis AI in the Sky: Developing Real -Time UAV Recognition Systems to Enhance Military Security

Reference 8

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This paper cites AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics.

Predicting Blood Type: Assessing Model Performance with ROC Analysis AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics

Reference 9

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Predicting Blood Type: Assessing Model Performance with ROC Analysis AI-Driven UAV Distinction: Leveraging Advanced Machine Learning

Reference 10

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This paper cites An advanced emergency warning message scheme based on vehicles speed and traffic densities.

Predicting Blood Type: Assessing Model Performance with ROC Analysis An advanced emergency warning message scheme based on vehicles speed and traffic densities

Reference 11

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This paper cites Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

Reference 12

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This paper cites Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

Reference 13

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Automated quantification of vesicoureteral reflux using machine learning with advancing diagnostic precision

Reference 14

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Automating Web Data Collection: Challenges, Solutions, and Python -Based Strategies for Effective Web Scraping

Reference 15

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This paper cites Mahmuddin ASCA.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Mahmuddin ASCA

Reference 16

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Classifying Psychiatric Patients Using Machine Learning

Reference 17

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This paper cites Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis

Reference 18

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This paper cites Comparative performance of ensemble models in predicting dental provider types: insights from fee -for-service data.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Comparative performance of ensemble models in predicting dental provider types: insights from fee -for-service data

Reference 19

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Comparative Study of Classification Mechanisms of Machine Learning on Multiple Data Mining Tool Kits

Reference 20

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This paper cites Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats

Reference 21

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This paper cites Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats

Reference 22

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Diabetes Prediction and Management Using Machine Learning Approaches

Reference 23

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Distributed quadtree overlay for resource discovery in shared computing infrastructure

Reference 24

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Doctor Adam Talib’s Public Relations Strategy in Improving the Quality of Patient Service

Reference 25

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Dynamic network topology for resource discovery in shared computing infrastructure

Reference 26

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This paper cites Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods

Reference 27

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This paper cites Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease

Reference 28

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Enhanced Resource Discovery Algorithm for Efficient Grid Computing

Reference 29

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux

Reference 30

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Enhancing Image Cryptography Performance with Block Left Rotation Operations

Reference 31

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Enhancing Internet -based Resource Discovery: The Efficacy of Distributed Quadtree Overlay

Reference 32

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends

Reference 33

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Evaluation of knowledge quality in the E -learning system

Reference 34

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Predicting Blood Type: Assessing Model Performance with ROC Analysis From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection

Reference 35

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Predicting Blood Type: Assessing Model Performance with ROC Analysis From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health

Reference 36

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Gene Microarray Cancer classification using correlation based feature selection algorithm and rules classifiers

Reference 37

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Guardians of the Web: Harnessing Machine Learning to Combat Phishing Attacks

Reference 38

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment

Reference 39

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction

Reference 40

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Innovative Machine Learning Solutions for Automated Kidney Tumor Detection in CT Imaging Through Comparative Analysis

Reference 41

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals

Reference 42

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Internet of things between reality or a wishing - list : a survey

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis

Reference 44

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Nodexl Tool for Social Network Analysis

Reference 45

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition

Reference 46

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Optimizing Resource Discovery in Grid Computing: A Hierarchical and Weighted Approach with Behavioral Modeling

Reference 47

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Peer to Peer Resource Discovery Mechanisms in Grid Computing : A Critical Review

Reference 48

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan

Reference 49

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Predicting Blood Type: Assessing Model Performance with ROC Analysis Phishing Website Detection Using Machine Learning

Reference 50

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This paper cites Prostate Cancer Detection and Analysis using Advanced Machine Learning.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Prostate Cancer Detection and Analysis using Advanced Machine Learning

Reference 51

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This paper cites Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research

Reference 52

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Observation dc0aff1b-3e28-45c7-86df-b0e31b304d4f · outbound

This paper cites Real -Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Real -Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:50.979393Z

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.

Observation d27f0bc3-34c5-487a-bce1-9b880d727ab9 · outbound

This paper cites Resource discovery mechanisms in shared computing infrastructure: A survey.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Resource discovery mechanisms in shared computing infrastructure: A survey

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:45.519987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e03372e6-52b9-45a3-8632-281870c3d81d · outbound

This paper cites Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:45.571799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32937f23-a7ca-4710-ab67-4b71c9f0ced2 · outbound

This paper cites Skywatch: Advanced Machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Skywatch: Advanced Machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:50.712797Z

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.

Observation 419c2766-88ec-4c40-8fdf-4f0b86a75d16 · outbound

This paper cites Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:50.557287Z

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.

Observation 5085b9c0-70fa-4cf7-bd34-a0973ba6e903 · outbound

This paper cites Survey on Patient Health Monitoring System Based on Internet of Things.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Survey on Patient Health Monitoring System Based on Internet of Things

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:45.762313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41409b88-16ed-45a0-bf27-39a4e40f4f87 · outbound

This paper cites Technological Innovations in Autonomous Vehicles: A Focus on Sensor Fusion and Environmental Perception.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Technological Innovations in Autonomous Vehicles: A Focus on Sensor Fusion and Environmental Perception

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:45.798347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0371ccc1-e0e0-4ef2-9acc-5fbd85b845a6 · outbound

This paper cites an unresolved cited work.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:45.837106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bae6bab2-6f24-4263-8fc6-d3b112024720 · outbound

This paper cites The modern hosting computing systems for small and medium businesses.

Predicting Blood Type: Assessing Model Performance with ROC Analysis The modern hosting computing systems for small and medium businesses

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:45.887252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2560982b-ace8-48b2-8c77-82314ac67956 · outbound

This paper cites The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector.

Predicting Blood Type: Assessing Model Performance with ROC Analysis The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:50.406112Z

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.

Observation 8e60547d-def7-47b8-9a95-5235a223f4c8 · outbound

This paper cites The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector.

Predicting Blood Type: Assessing Model Performance with ROC Analysis The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:46.021659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a5c627f-6e74-4f60-867f-3ea2422afef0 · outbound

This paper cites Toward achieving self -resource discovery in distributed systems based on distributed quadtree.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Toward achieving self -resource discovery in distributed systems based on distributed quadtree

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:50.100860Z

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.

Observation 854ada2d-a7c7-4415-b94b-69dd2670b4b4 · outbound

This paper cites The Two Sides of AI in Cybersecurity: Opportunities and Challenges.

Predicting Blood Type: Assessing Model Performance with ROC Analysis The Two Sides of AI in Cybersecurity: Opportunities and Challenges

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:50.260951Z

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.

Observation a10b7b06-e55a-4e7c-9636-6fc9f23619a6 · outbound

This paper cites Towards Self -Organizing Infrastructure : A New Architecture for Autonomic Green Cloud Data Centers.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Towards Self -Organizing Infrastructure : A New Architecture for Autonomic Green Cloud Data Centers

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:49.831885Z

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.

Observation df059387-d01a-44c0-8829-8f4ae9560d6d · outbound

This paper cites Towards autonomic overlay self -load balancing.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Towards autonomic overlay self -load balancing

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:49.951084Z

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.

Observation 0da57e98-d497-46a5-98d7-54ebf2451349 · outbound

This paper cites STUDY OF FINGERPRINT PATTERNS IN RELATIONSHIP WITH BLOOD GROUP AND GENDER IN SAURASHTRA REGION.

Predicting Blood Type: Assessing Model Performance with ROC Analysis STUDY OF FINGERPRINT PATTERNS IN RELATIONSHIP WITH BLOOD GROUP AND GENDER IN SAURASHTRA REGION

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:49.598026Z

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.

Observation 6092103d-6640-4152-ae5b-0712c24bdf62 · outbound

This paper cites Towards self -resource discovery and selection models in grid computing.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Towards self -resource discovery and selection models in grid computing

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:49.700407Z

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.

Observation 54636d7a-9032-42c2-8a9f-5dd084f999e9 · outbound

This paper cites Distribution of ABO & Rh bloog group in relation to dermatoglyphics and BMI.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Distribution of ABO & Rh bloog group in relation to dermatoglyphics and BMI

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:49.405683Z

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.

Observation dff606bf-bb9c-4225-96c6-6214c5386df8 · outbound

This paper cites an unresolved cited work.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:54:49.479854Z

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.

Observation 48957b4b-6eb9-4095-b7ce-7a48d7a79487 · outbound

This paper cites an unresolved cited work.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:54:49.200134Z

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.

Observation ffd97c79-80fc-46c7-9654-ce49bcbefd8c · outbound

This paper cites Gender Specific Correlation between Lip Print, Fingerprint and Blood Groups among Adults aged 20-30 Years attending a Tertiary Health Care Centre.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Gender Specific Correlation between Lip Print, Fingerprint and Blood Groups among Adults aged 20-30 Years attending a Tertiary Health Care Centre

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:49.306200Z

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.

Observation 33beb87b-55fb-4eb0-bcd3-18faa1488ed3 · outbound

This paper cites Qualitative Analysis of Pattern of Finger Print in Relation to Gender and Blood Group.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Qualitative Analysis of Pattern of Finger Print in Relation to Gender and Blood Group

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:49.003080Z

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.

Observation b4da8f99-67b1-409f-ae35-ae739a78af66 · outbound

This paper cites Kavita V Horadi.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Kavita V Horadi

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:49.098945Z

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.

Observation c6f057da-3c33-41e7-83c6-50c2861df269 · outbound

This paper cites Correlation of lip print, finger print and blood groups in a Tamil Nadu based population.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Correlation of lip print, finger print and blood groups in a Tamil Nadu based population

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:48.752806Z

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.

Observation 0f07fcf3-e411-4ada-b88d-a6341080a876 · outbound

This paper cites MAHALINGAM BHUVANESWARI, DR.

Predicting Blood Type: Assessing Model Performance with ROC Analysis MAHALINGAM BHUVANESWARI, DR

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:48.898612Z

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.

Observation 3d95b080-e289-4b8e-94ff-eab7b78fc80c · outbound

This paper cites Evaluation of Correlation between Blood Group System & Fingerprint Classification System in both female and male.

Predicting Blood Type: Assessing Model Performance with ROC Analysis Evaluation of Correlation between Blood Group System & Fingerprint Classification System in both female and male

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:54:48.635952Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers