Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T19:57:12.338110Z
Paper Citation Record · LEDGER
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network
As of 20 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2508.11462.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T11:20:33.347093Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T11:20:44.023401Z
Reference resolution
26 of 26 outbound references displayed
- verified exact0
- verified fuzzy26
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 11f953c2-156e-47ff-a888-1b01f6ad2d0c · outbound
This paper cites Deep learning–based segmentation of glomerular basement membrane in electron microscopy images,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Deep learning–based segmentation of glomerular basement membrane in electron microscopy images,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.634331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0514c97a-4d77-40ac-a0c1-6cb158df3b54 · outbound
This paper cites Glomerular basement membrane thickness in diabetic nephropathy: a stereo- logical study,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Glomerular basement membrane thickness in diabetic nephropathy: a stereo- logical study,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.623060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 58ae861b-5e2d-449f-9fb5-b1c27d396195 · outbound
This paper cites The glomerular filtration barrier: ultrastructure and functional implications,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network The glomerular filtration barrier: ultrastructure and functional implications,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.613208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 464be930-9f60-4c92-8636-b6177aff4c94 · outbound
This paper cites Alport’s syndrome, goodpasture’s syn- drome, and type iv collagen,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Alport’s syndrome, goodpasture’s syn- drome, and type iv collagen,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.603625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b9a4f407-2dee-4eed-883d-fe055f829cbd · outbound
This paper cites The ultrastructural disruption of the glomerular basement membrane in diabetic nephropathy revealed by “tissue negative staining method.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network The ultrastructural disruption of the glomerular basement membrane in diabetic nephropathy revealed by “tissue negative staining method
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.592979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 65e3124e-0b62-42ae-aa52-6f7bf575d992 · outbound
This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.582394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e35c8d42-61b3-42af-888b-7d791120451e · outbound
This paper cites Deep learning-based morphological feature extraction for kidney pathology,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Deep learning-based morphological feature extraction for kidney pathology,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.571934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 264ea701-e9d9-42a9-a730-140988bd0d63 · outbound
This paper cites A novel approach to the classification of glomerular diseases: the nephrotic syndrome study network,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network A novel approach to the classification of glomerular diseases: the nephrotic syndrome study network,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.561731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 41c559e7-7e44-4bcc-a0ac-e8f03d337420 · outbound
This paper cites A survey on deep learning in medical image analysis,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network A survey on deep learning in medical image analysis,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.551870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 99563864-951e-417e-98c8-4adf6b1b5626 · outbound
This paper cites Prototypical networks for few-shot learning,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Prototypical networks for few-shot learning,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.541825Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e87273e3-c3a4-49ac-8757-46531e420144 · outbound
This paper cites One-shot learning for semantic segmentation,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network One-shot learning for semantic segmentation,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.531804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8cd9ad61-64dc-4cad-8728-6541459603bc · outbound
This paper cites Generalizing from a few examples: A survey on few-shot learning,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Generalizing from a few examples: A survey on few-shot learning,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.522599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 17117637-d6a9-4d55-a620-2371be92a988 · outbound
This paper cites Segment anything model 2 (sam-2): Scaling up zero-shot image segmentation,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Segment anything model 2 (sam-2): Scaling up zero-shot image segmentation,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.512063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f8f6234b-54cd-4e73-9e9a-3ddf79610cc4 · outbound
This paper cites All-in- sam: from weak annotation to pixel-wise nuclei segmentation with prompt-based finetuning,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network All-in- sam: from weak annotation to pixel-wise nuclei segmentation with prompt-based finetuning,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.502272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f9689225-88aa-4cb6-b963-30716c273333 · outbound
This paper cites Sam-med2d: Segment anything model for medical image segmentation,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Sam-med2d: Segment anything model for medical image segmentation,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.490963Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 793aaf7e-de31-4ca2-9130-d36646b32019 · outbound
This paper cites Leverage weekly annotation to pixel-wise annotation via zero-shot segment anything model for molecular-empowered learning,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Leverage weekly annotation to pixel-wise annotation via zero-shot segment anything model for molecular-empowered learning,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.480981Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a460a475-0739-4779-a40f-b97d96f69df2 · outbound
This paper cites Can sam segment medical images? an extensive benchmark study on 12 datasets,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Can sam segment medical images? an extensive benchmark study on 12 datasets,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.470499Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b2bfdaa3-d545-48a9-92ba-072df1bcf8cc · outbound
This paper cites Prompt, segment, and learn: Boosting medical image segmentation with segment anything model,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Prompt, segment, and learn: Boosting medical image segmentation with segment anything model,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.460710Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c77d7376-74ee-46b2-8fa1-95b33013ac10 · outbound
This paper cites Prompt-to-prompt image segmentation with foundation models,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Prompt-to-prompt image segmentation with foundation models,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.450222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f12ccb7e-d5c6-4166-9f90-0989cabbba4d · outbound
This paper cites Sam �: Prompt learning for efficient interactive segmentation,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Sam �: Prompt learning for efficient interactive segmentation,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.439939Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3cba44a0-776c-4078-8d17-4090e0307b53 · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network U-net: Convolutional networks for biomedical image segmentation,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.428657Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a7444d28-f183-4013-b606-43b140d1dd84 · outbound
This paper cites Swin unetr: Swin trans- formers for semantic segmentation of brain tumors in mri images,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Swin unetr: Swin trans- formers for semantic segmentation of brain tumors in mri images,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.417042Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 95674f1e-51df-4b8c-a207-00973efd6e0c · outbound
This paper cites Rethinking atrous convolution for semantic image segmen- tation,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Rethinking atrous convolution for semantic image segmen- tation,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.405958Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation bde306e4-ae0b-4c1e-af0c-f45ea5a62f5e · outbound
This paper cites Universeg: Universal medical image segmentation,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Universeg: Universal medical image segmentation,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.394815Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c7a9b97f-1038-436e-adbd-c79405f368fd · outbound
This paper cites Gbmseg: Prompting segment anything for glomerular basement membrane segmentation in em images,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Gbmseg: Prompting segment anything for glomerular basement membrane segmentation in em images,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.383594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 37361305-d5e0-487b-9ad1-fb6e79aca97b · outbound
This paper cites Dinov2: Learning robust visual features without supervision,.
Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network Dinov2: Learning robust visual features without supervision,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T19:57:12.371203Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
Observation 12fd2b17-d869-4a89-950b-29e2455ac452 · inbound
Low-frequency observations of low-mass binary systems with neutron star candidates cites this paper.
Low-frequency observations of low-mass binary systems with neutron star candidates Search of RRATs on declinations from $+42^{\circ}$ to $+55^{\circ}$ with a neural network
Reference 19
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-05T11:20:44.028091Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.