{"id":"W4400486093","doi":"10.61091/jcmcc120-10","title":"RICE: A Dataset and Baseline for Cloud Removal in Remote Sensing Images","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Baseline (sea); Cloud computing; Remote sensing; Environmental science; Computer science; Geography; Geology; Operating system; Oceanography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007744993,0.001943299,0.0009346157,0.001999259,0.0008084252,0.0009529568,0.002323706,0.001908372,0.002818193],"category_scores_gemma":[0.00150151,0.0003459031,0.001614789,0.002040351,0.0008010681,0.0009416663,0.001300829,0.001700337,0.004310591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037199,"about_ca_system_score_gemma":0.001304266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02322949,"about_ca_topic_score_gemma":0.03721202,"domain_scores_codex":[0.9992028,0.00008438269,0.00006633242,0.0002670671,0.0002539268,0.0001255243],"domain_scores_gemma":[0.9992674,0.00007535178,0.00006228501,0.0002938189,0.0002205339,0.00008059741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001839208,0.003028918,0.0223649,0.002703174,0.0007270906,0.001437739,0.0002887705,0.07835542,0.05733119,0.003547303,0.5636184,0.2647579],"study_design_scores_gemma":[0.001300093,0.001330544,0.1099316,0.0006168967,0.000497145,0.003881702,0.001245446,0.361994,0.07835342,0.007659921,0.4326802,0.0005090978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3580425,0.004549243,0.06411637,0.002686643,0.00118885,0.003012685,0.5092866,0.03262608,0.024491],"genre_scores_gemma":[0.1684824,0.0009982295,0.06828859,0.0006502625,0.0001632925,0.0009215804,0.753092,0.000801037,0.006602659],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02322949,"threshold_uncertainty_score":0.04618853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613653581832174,"score_gpt":0.2685562967376969,"score_spread":0.2524197609193751,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}