Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-10T19:11:09.863369Z
Paper Citation Record · LEDGER
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems
As of 4 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 2 inbound Pith citation observations for arXiv:2604.05057.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T22:04:30.140548Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-06-30T22:05:05.568775Z
Reference resolution
18 of 18 outbound references displayed
- verified exact0
- verified fuzzy13
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation bac1dfd4-013e-4e06-86b0-36184d8c2563 · outbound
This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Reference 1
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-10T23:25:49.596677Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1fdb370b-cebc-4ff8-adc7-0d878027e001 · outbound
This paper cites Brown, T.
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.221771Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5bd63180-3d29-40cb-9fb9-a73aa57279c4 · outbound
This paper cites A tutorial on human activity recognition using body-worn inertial sensors.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems A tutorial on human activity recognition using body-worn inertial sensors
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.216222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0bea4e9f-c121-4104-a327-21fda7d57f61 · outbound
This paper cites Nonparametric estimation of the number of classes in a population.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Nonparametric estimation of the number of classes in a population
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.210803Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5156d2d1-2a43-4cde-a0b6-93dab423262e · outbound
This paper cites Church and William A.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Church and William A
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.219049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a2845f28-e541-4938-90ae-9fa2441516a3 · outbound
This paper cites Estimating the number of unseen species: How many words did S hakespeare know? Biometrika, 63 0 (3): 0 435--447.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Estimating the number of unseen species: How many words did S hakespeare know? Biometrika, 63 0 (3): 0 435--447
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.225062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3a76db34-cc5a-479e-9681-dac6daee8e20 · outbound
This paper cites an unresolved cited work.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-05-16T08:02:35.213387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6044abfd-4699-44ed-9efc-a648b68699c4 · outbound
This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems A baseline for detecting misclassified and out-of-distribution examples in neural networks
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.248067Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ae5198cb-c589-4db9-8dae-6571670e491d · outbound
This paper cites Lara and Miguel A.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Lara and Miguel A
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.242454Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 01e2d2a9-ae14-414e-9d7b-c6b0d55ef2d4 · outbound
This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.245110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 75deb7e3-fa10-4bf3-855a-5531019adf4f · outbound
This paper cites Owens, and Yixuan Li.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Owens, and Yixuan Li
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.236790Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 870cd0b0-83ff-4fc0-9f69-f15ccc61fd61 · outbound
This paper cites Optimal prediction of the number of unseen species.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Optimal prediction of the number of unseen species
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.239634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a5a7b65c-8813-44f1-9850-9fff7fdc6604 · outbound
This paper cites an unresolved cited work.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-05-16T08:02:35.230817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ca1bbc56-b621-431c-908a-64c867b7d1b3 · outbound
This paper cites Introducing a new benchmarked dataset for activity monitoring.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Introducing a new benchmarked dataset for activity monitoring
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.253871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c789c71e-ad35-4599-910e-2db757c15337 · outbound
This paper cites Creating and benchmarking a new dataset for physical activity monitoring.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Creating and benchmarking a new dataset for physical activity monitoring
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.256580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ea25955a-100b-4024-9bcb-f609520b68bb · outbound
This paper cites TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T08:02:35.250932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 504c9ad0-e9e7-4a24-9d56-9329cd0558eb · outbound
This paper cites an unresolved cited work.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Unresolved cited work
Reference 17
- Resolution
- unresolved
- raw_fallback, observed 2026-05-16T08:02:35.227864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cb4bf87a-d4ef-4819-bafa-4ba4a6b9ea7b · outbound
This paper cites an unresolved cited work.
Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-05-16T08:02:35.233547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation 446b5282-0c66-4505-a757-e3fd0f80c1ee · inbound
NOVA: Fundamental Limits of Knowledge Discovery Through AI cites this paper.
NOVA: Fundamental Limits of Knowledge Discovery Through AI Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-05-19T17:22:42.050243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2d8b6111-cd5e-43b2-8628-422d50778c7b · inbound
NOVA: Fundamental Limits of Knowledge Discovery Through AI cites this paper.
NOVA: Fundamental Limits of Knowledge Discovery Through AI Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T22:05:05.570231Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.