Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T13:48:06.745312Z
Paper Citation Record · LEDGER
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan
As of 24 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2411.15923.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
42 of 42 outbound references displayed
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- verified fuzzy40
- unresolved2
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 5c5175cb-7eab-4f55-a156-d53a9ac1f6b2 · outbound
This paper cites Unlocking large-scale crop field delineation in small holder farming systems with transfer learning and weak supervision.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Unlocking large-scale crop field delineation in small holder farming systems with transfer learning and weak supervision.,
Reference 1
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 053a4a1b-b378-42dd-a66a-e809676e9202 · outbound
This paper cites Adeel, M.
Reference 2
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ce97d8c5-211a-4b5c-a5e2-e031e3034bc1 · outbound
This paper cites Agricultural Field Boundary Delineation Using Deep Learning Techniques.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Agricultural Field Boundary Delineation Using Deep Learning Techniques.,
Reference 3
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 928ca666-ed61-4f6a-bc4c-19ee6bbdc928 · outbound
This paper cites Delineation of crop field areas and boundaries from UAS imagery using PBIA and GEOBIA with random forest classification.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Delineation of crop field areas and boundaries from UAS imagery using PBIA and GEOBIA with random forest classification.,
Reference 4
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8d368113-6400-490e-b3ab-b9478c9df2ab · outbound
This paper cites Cai, Yaping, et al.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Cai, Yaping, et al
Reference 5
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 60a0a93c-9cb4-4a6c-840a-76184c86485c · outbound
This paper cites Farmer a ttitudes to the use of sensors and automation in fertilizer decision -making: Nitrogen fertilization in the Australian grains secto,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Farmer a ttitudes to the use of sensors and automation in fertilizer decision -making: Nitrogen fertilization in the Australian grains secto,
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1c19ae80-4248-4c4d-a364-8f01cede44ee · outbound
This paper cites Should increasing the field size of monocultural crops be expected to exacerbate pest damage?.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Should increasing the field size of monocultural crops be expected to exacerbate pest damage?.,
Reference 7
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3035c8d5-323a-4128-bc6d-07ea3c8317a5 · outbound
This paper cites Bringing diversity back to agriculture: Smaller fields and non-crop elements enhance biodiversity in intensively managed arable farmlands,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Bringing diversity back to agriculture: Smaller fields and non-crop elements enhance biodiversity in intensively managed arable farmlands,
Reference 8
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3adef677-6efa-4588-8c6a-108df06fc053 · outbound
This paper cites A machine learning approach for agricultural parcel delineation through agglomerative segmentation,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan A machine learning approach for agricultural parcel delineation through agglomerative segmentation,
Reference 9
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 49b2c881-7ee4-4fb6-9557-3c621f5507af · outbound
This paper cites Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Segmentation of Agricultural Parcels in Satellite Images Based on Historical Vegetation Index Data.,
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4cb08310-5d72-4df4-9e9c-87fc27a2db1d · outbound
This paper cites Automated crop field extraction from multi -temporal Web Enabled Landsat Data.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Automated crop field extraction from multi -temporal Web Enabled Landsat Data.,
Reference 11
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c529fbec-572c-40a9-8728-a0fbf408299b · outbound
This paper cites Automated farm field delineation and crop row detection from satellite images,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Automated farm field delineation and crop row detection from satellite images,
Reference 12
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 41c10ccb-b48b-4828-8550-a19bb4705d73 · outbound
This paper cites Supervised Learning of Edges and Object Boundaries,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Supervised Learning of Edges and Object Boundaries,
Reference 13
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2a8a4899-92f8-4462-ac45-b8c7a92f622a · outbound
This paper cites Fast edge detection using structured forests.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Fast edge detection using structured forests.,
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 2026-08-23T06:30:58.430688+00:00.
Observation 2c145879-da34-4ab7-b397-4f0b10a71ebf · outbound
This paper cites Yang, Ruoyu, et al.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Yang, Ruoyu, et al
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T13:48:07.431425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7cf2ab7f-30fb-49cd-a7d4-cd42c3202b72 · outbound
This paper cites an unresolved cited work.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Unresolved cited work
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 60fbe790-e484-4b24-8ff0-3ce1168bbcfb · outbound
This paper cites Agricultural Field Boundary Delineation with Satellite Image Segmentation for High -Resolution Crop Mapping: A Case Study of Rice Paddy.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Agricultural Field Boundary Delineation with Satellite Image Segmentation for High -Resolution Crop Mapping: A Case Study of Rice Paddy.,
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 55cec68d-0b96-41ba-b31b-32ef24616a79 · outbound
This paper cites Convolutional oriented boundaries: From image segmentation to high-level tasks,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Convolutional oriented boundaries: From image segmentation to high-level tasks,
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8ef99b8f-4157-4ff5-bb02-5796e99f0763 · outbound
This paper cites A deep learning approach to the classification of sub-decimetre resolution aerial images.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan A deep learning approach to the classification of sub-decimetre resolution aerial images.,
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b979ff43-7bc1-49ae-8eda-ae15cdd24b09 · outbound
This paper cites Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping.,
Reference 20
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 80c097f4-1532-4ae1-9c97-e1492087efcb · outbound
This paper cites Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super -Resolution Contour Detector Based on Fully Convolutional Networks,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super -Resolution Contour Detector Based on Fully Convolutional Networks,
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 70b3f2c8-62ca-4cd2-acfd-2490f88e25e5 · outbound
This paper cites A comparison of object -based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan A comparison of object -based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery,
Reference 22
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7132d7e1-4e87-4c99-b21e-9fcb417a57f5 · outbound
This paper cites Deep Learning on High Spatial and Temporal Cadence Satellite Imagery for Field Boundary Delineation,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Deep Learning on High Spatial and Temporal Cadence Satellite Imagery for Field Boundary Delineation,
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation fd1ec40d-6a14-4408-864e-a9a2bef908df · outbound
This paper cites Deep learning for automatic outlining agricultural parcels: Exploiting the land parcel identification system.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Deep learning for automatic outlining agricultural parcels: Exploiting the land parcel identification system.,
Reference 24
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6054e91d-3815-414e-b2a7-d2de9815fff6 · outbound
This paper cites Aung, Han Lin, et al.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Aung, Han Lin, et al
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 84a64b19-c54d-44e1-a1b0-1f9bb2a70d82 · outbound
This paper cites Philipp FISCHER a Thomas BROX,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Philipp FISCHER a Thomas BROX,
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e9d3a7c1-6f18-47e7-9c6a-eaf630cba874 · outbound
This paper cites Comparison of Backbones for Semantic Segmentation Network,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Comparison of Backbones for Semantic Segmentation Network,
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 2026-08-23T06:30:58.430688+00:00.
Observation 5da124aa-842c-41b3-8257-85b236992676 · outbound
This paper cites Road extraction from high -resolution remote sensing imagery using deep learning,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Road extraction from high -resolution remote sensing imagery using deep learning,
Reference 28
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1dcc39bd-cd2f-4e6d-8d7a-20c0506d84d3 · outbound
This paper cites Geo services -PDOK,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Geo services -PDOK,
Reference 29
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 48532ae1-e5a0-4ea4-8e91-45284f5ffffa · outbound
This paper cites Wider or deeper: Revisiting the resnet model for visual recognition.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Wider or deeper: Revisiting the resnet model for visual recognition.,
Reference 30
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8ce75852-7fd2-4fbb-beb6-5d1b8c2b2d2a · outbound
This paper cites Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet -152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet -152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification,
Reference 31
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b6b75c93-950c-4da9-aaa7-1d3ef5db0589 · outbound
This paper cites An efficient brain tumor image segmentation based on deep residual networks (ResNets),.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan An efficient brain tumor image segmentation based on deep residual networks (ResNets),
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4dcccdbd-2426-4b28-a692-ef644ee514bf · outbound
This paper cites ontextual band addition and m ulti-look inferencing to improve semantic segmentation model performance on satellite images,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan ontextual band addition and m ulti-look inferencing to improve semantic segmentation model performance on satellite images,
Reference 33
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f1d73b9b-e73e-493d-b8b4-fd539d63fd6e · outbound
This paper cites Application of deep learning for delineation of visible cadastral bound aries from remote sensing imagery,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Application of deep learning for delineation of visible cadastral bound aries from remote sensing imagery,
Reference 34
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation dc6cb880-53ca-4871-a310-fbd01ecdc7f1 · outbound
This paper cites Inconsistencies in Cadastral Boundary Data—Digitisation and Maintenance.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Inconsistencies in Cadastral Boundary Data—Digitisation and Maintenance.,
Reference 35
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e336aa3e-45cc-456c-aa7e-512ea94f3eef · outbound
This paper cites Improving field boundary delineation in ResUNets via adversarial deep learning,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Improving field boundary delineation in ResUNets via adversarial deep learning,
Reference 36
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1c4ac39c-a6f4-4070-9664-85edac365429 · outbound
This paper cites an unresolved cited work.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Unresolved cited work
Reference 37
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T13:48:06.894518Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e10e511f-7e66-4359-8ee2-46103c632e69 · outbound
This paper cites U -SSD: Improved SSD based on U -Net architecture for end -to-end table detection in document images.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan U -SSD: Improved SSD based on U -Net architecture for end -to-end table detection in document images.,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T13:48:06.875443Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a7f4212d-72a8-42b7-bfc2-8635da7b17d0 · outbound
This paper cites Detecting functional field units from satellite images in smallholder farming systems using a deep learning based computer vision approach: A case study from Bangladesh,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Detecting functional field units from satellite images in smallholder farming systems using a deep learning based computer vision approach: A case study from Bangladesh,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T13:48:06.857976Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4c9fc256-54d1-41ed-aab7-4c87f96a6fe1 · outbound
This paper cites A case study for updating land parcel identification systems (IACS) by means of remote sensing.,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan A case study for updating land parcel identification systems (IACS) by means of remote sensing.,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T13:48:06.838946Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 95133018-373c-4a76-8420-aa84d86398e9 · outbound
This paper cites Land Parcel Identification System (LPIS) Anomalies' Sampling and Spati al Pattern: Towards convergence of ecological methodologies and GIS technologies,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan Land Parcel Identification System (LPIS) Anomalies' Sampling and Spati al Pattern: Towards convergence of ecological methodologies and GIS technologies,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T13:48:06.817075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 998bbd5f-14bb-4915-80a7-a57679eaff73 · outbound
This paper cites U -SSD: Improved SSD based on U -Net architecture for end -to-end table detection in document images,.
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan U -SSD: Improved SSD based on U -Net architecture for end -to-end table detection in document images,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T13:48:06.794952Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Pith citing papers
No inbound Pith citation observations are available.