{"as_of":"2026-08-08T17:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:250a06bade8f4e04d90d2642151bda0104e2d4a89360281b98750b3a0a2197f4","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:09:58.028346Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.16033/citation-record","integrity":"/paper/2505.16033/integrity","json":"/paper/2505.16033/citation-record.json","paper":"/paper/2505.16033"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:10:00.386846Z","title":"The resource outlook to 2050: by how much do land, water and crop yields need to increase by 2050? expert meeting on how to feed the world in 2050.http://www","venue":null,"work_id":"9f96ecec-f1b0-4b3f-8a53-0cb1a9677731","year":2009},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:56.714082Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:1fcccb4caa5ec14e3a426dd33b43809e4301575892722755fda1cc587fbc6519","observation_id":"38eed9f2-efd3-4bd7-aea6-2d88aa52049b","resolution":{"observed_at":"2026-08-07T15:10:00.412210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:10:00.242442Z","title":"Plant leaf disease recognition using depth-wise separable convolution-based models.Symmetry, 13(3):511, 2021","venue":null,"work_id":"8dc49cea-c832-4da6-835f-3463dcf882bf","year":2021},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:56.761338Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:2eacd0bdb3e1c617ddb48564c7c6727b55051bdeb4f28734baadf4e174b46138","observation_id":"1926e7d1-8677-4aa6-88e6-7c73d150ac5b","resolution":{"observed_at":"2026-08-07T15:10:00.308661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:10:00.038849Z","title":"Transfer learning- based deep ensemble neural network for plant leaf disease detection.Journal of Plant Diseases and Protection, 129(3):545–558, 2022","venue":null,"work_id":"3b13dd52-1c3f-4543-a851-4405a75e1f2f","year":2022},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:56.839965Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:19adebeaf2ae3905c710569d01f23af2eabc47a9092d14d5f9bc44e06b3b5420","observation_id":"d656114a-b13f-4f32-acce-8b0b8dc63cdc","resolution":{"observed_at":"2026-08-07T15:10:00.140018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.08060","last_updated":"2016-04-12T01:26:18Z","snapshot_observed_at":"2026-07-06T04:37:45.149058Z","submitted_at":"2015-11-25T13:51:29Z","title":"An open access repository of images on plant health to enable the development of mobile disease diagnostics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.08060","snapshot_observed_at":"2026-08-07T15:09:56.888941Z","title":"An open access repository of images on plant health to enable the development of mobile disease diagnostics.arXiv preprint arXiv:1511.08060, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:56.888941Z"},"links":{"cited_paper":"/paper/1511.08060","citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:0fa9abdff0ad819c9272751e5fcfaa55ff8f055c8eeb40f8cbbd5fb184348de0","observation_id":"ce65a79e-faa1-4499-b698-2540982a8054","resolution":{"observed_at":"2026-08-07T15:09:56.888941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:59.782136Z","title":"High performance deep learning architecture for early detection and classification of plant leaf disease.Journal of Agriculture and Food Research, 14:100675, 2023","venue":null,"work_id":"b97abc75-eb3d-4f53-b5cd-99a0092fc830","year":2023},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:56.940171Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:d5c383ec66f616d1f3741257ed1e8f1f72238e37f60a06f7ed7728aa5121fc06","observation_id":"25253991-19ca-42c3-a31a-eb22bc519a7a","resolution":{"observed_at":"2026-08-07T15:09:59.908899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:59.614971Z","title":"Plant disease detection in imbalanced datasets using efficient convolutional neural networks with stepwise transfer learning.IEEE Access, 9:140565– 140580, 2021","venue":null,"work_id":"9cb95fcd-6c5a-41eb-b7e1-50d988fbcb9e","year":2021},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.006307Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:586102190c6c647a492b72501e0545fae60cf27e1a7a75d6ae43ff9352aa3e96","observation_id":"a9a95875-d6c0-4a48-8b1e-2129b3d530d1","resolution":{"observed_at":"2026-08-07T15:09:59.698372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:59.452115Z","title":"Comparative analysis of cnn, efficientnet and resnet for grape and potato leaves disease prediction: A deep learning approach","venue":null,"work_id":"75976abb-b329-436d-9a74-5493cc9f54d2","year":2024},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.058638Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:4ad1df20e3bd6da54a13edff2e00e8e0aec03a22a8013afc8620018797342c86","observation_id":"93ea19a3-b1af-473a-a9f5-884f2005f025","resolution":{"observed_at":"2026-08-07T15:09:59.529169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:59.323333Z","title":"Botanicx-ai: Identification of tomato leaf diseases using an explanation-driven deep-learning model.Journal of Imaging, 9(2):53, 2023","venue":null,"work_id":"1039bfe0-30a8-4376-848f-505ae47ee6ad","year":2023},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.138301Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:75854af04ea2f2e8b85c221d66d2a328aefa5acd95d6ed6fec00c6a5c1a615ea","observation_id":"f56ce7a9-01e4-4103-8a1e-f86a58f3c7cf","resolution":{"observed_at":"2026-08-07T15:09:59.367674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:59.206577Z","title":"Explainable ai for tomato leaf disease detection: Insights into model interpretability","venue":null,"work_id":"12ad1694-2c9c-4521-933e-0206867b16b6","year":2023},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.179161Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:f8129613a6252ce4f757ad53dcd2970d6831ddc9ef7aedd45cbfaf2fce93d94f","observation_id":"674e54fd-d8e3-48db-a208-f546fbefbb3d","resolution":{"observed_at":"2026-08-07T15:09:59.235364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:59.120708Z","title":"Explainable deep learning model for automatic mulberry leaf disease classification.Frontiers in Plant Science, 14:1175515, 2023","venue":null,"work_id":"3ce4eb30-ad02-40eb-8650-8854af23055f","year":2023},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.249075Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:bee26783e380385465df5c88a88f6c113d6546e383c09c6a4ac675df67ee374c","observation_id":"adffdbfc-2b69-404c-9fe0-63d54d4667a2","resolution":{"observed_at":"2026-08-07T15:09:59.198022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:58.987443Z","title":"Explainable deep learning study for leaf disease classification.Agronomy, 12(5):1035, 2022","venue":null,"work_id":"48082df5-67e8-4c96-acd8-6f2103c43113","year":2022},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.353717Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:d6cd9a919e1bf2c9abf2b792d97405aef814d2c4bdd75bbe4f080809acf3d526","observation_id":"bc44b5c4-fc05-4cbb-80c3-777877b3c35d","resolution":{"observed_at":"2026-08-07T15:09:59.038507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:58.845118Z","title":null,"venue":null,"work_id":"2aa2e8be-7df3-42c8-b107-c16a3221e1b2","year":2022},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.419949Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:5a0731a26fd6f802b56766256d6a7747bebf4962972ee63a27f5d56f9879433e","observation_id":"57c942f1-2aec-4310-8010-f4605decb472","resolution":{"observed_at":"2026-08-07T15:09:58.909205Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:58.723178Z","title":"Plant leaf freshness and disease detection dataset from bangladesh, 2024","venue":null,"work_id":"f9af157d-f16f-40e5-b67b-8412b8097e0e","year":2024},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.487107Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:9737da50c4494b01b08d316f7d618edd20ef134b2cd4b44ee615c2493af12969","observation_id":"a84af8e0-3f86-4a9f-ae06-63292eccefef","resolution":{"observed_at":"2026-08-07T15:09:58.769359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:58.576293Z","title":"Comprehensive smart smartphone image dataset for plant leaf disease detection and freshness assessment from bangladesh vegetable fields.Data in Brief, 56:110775, 2024","venue":null,"work_id":"d6234e18-8c72-46fd-9d16-6edc2ca17a4f","year":2024},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.567654Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:1b876e10e7ba235a7b15212ba704d4ed75eae0e79cc42649f0e7ad61e66365ed","observation_id":"50752491-aafc-4e7a-a3f3-81d3cd0dab16","resolution":{"observed_at":"2026-08-07T15:09:58.639038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:58.461076Z","title":"A study of cnn and transfer learning in medical imaging: Advantages, challenges, future scope.Sustainability, 15(7):5930, 2023","venue":null,"work_id":"66e69f30-08a2-4119-b9a8-aff435feee8c","year":2023},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.668408Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:3e4d7f57911f15a09c0b70817adf804ee307836af6075b8b0906420949cb4207","observation_id":"11a77817-5915-4aee-a7c7-bccc6a2e09e6","resolution":{"observed_at":"2026-08-07T15:09:58.512690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:57.772406Z","title":"Keras.https://keras.io, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.772406Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:caaafa6fcc9d969b2926c885d5be9a0233c83d198639980436ffa969f52128e6","observation_id":"27fbb455-0832-49b5-afb0-5af6260b83d6","resolution":{"observed_at":"2026-08-07T15:09:57.772406Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:58.282537Z","title":null,"venue":null,"work_id":"5d922772-f84d-4c4a-98e1-a294c2851008","year":2015},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.862466Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:180fc6bb91e9d99e08bd73173a6565847daae4f81fc74d2755acb07bc28e4637","observation_id":"9d2b9739-2dde-4e30-8c79-0ac1350497d1","resolution":{"observed_at":"2026-08-07T15:09:58.369916Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:09:58.169515Z","title":"A deep learning-based bengali visual question answering system","venue":null,"work_id":"984ebeea-4ff3-4cf3-904d-426fb90da645","year":2022},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:57.929647Z"},"links":{"citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:e85b7f138a70c7f05e303db4da6211be4813259a03330e549a5e534fa2b511b7","observation_id":"4d8f2cd8-1204-4f38-a537-b12b36470e4c","resolution":{"observed_at":"2026-08-07T15:09:58.228382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07338","last_updated":"2024-05-12T17:21:57Z","snapshot_observed_at":"2026-07-06T18:13:12.014618Z","submitted_at":"2024-05-12T17:21:57Z","title":"Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07338","snapshot_observed_at":"2026-08-07T15:09:58.028346Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:09:58.028346Z"},"links":{"cited_paper":"/paper/2405.07338","citing_paper":"/paper/2505.16033"},"observation_digest":"sha256:f39b372bb77bc59776c119473e61fe16331804275a77df9c928e5515bf3bc5cf","observation_id":"fbbce8ba-b89f-463a-9a02-482173c48f7a","resolution":{"observed_at":"2026-08-07T15:09:58.028346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.16033","last_updated":"2025-06-19T19:07:13Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T15:06:01.152880Z","submitted_at":"2025-05-21T21:25:57Z","title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":14},"total_outbound_references":19},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2505.16033."}