{"id":"W4403867810","doi":"10.3390/rs16214015","title":"How to Learn More? Exploring Kolmogorov–Arnold Networks for Hyperspectral Image Classification","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Remote sensing; Pattern recognition (psychology); Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004701958,0.0005410623,0.0004000543,0.0005447348,0.0002104125,0.0007005236,0.0006230521,0.0006555548,0.0009416838],"category_scores_gemma":[0.001536313,0.0003077243,0.0005004057,0.0005064809,0.0006635067,0.001689587,0.0008339527,0.0009790019,0.0002988333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005486432,"about_ca_system_score_gemma":0.0005680277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004596679,"about_ca_topic_score_gemma":0.005662571,"domain_scores_codex":[0.9998295,0.00004040701,0.00001181622,0.0000459227,0.00004899143,0.00002335458],"domain_scores_gemma":[0.9996095,0.0001952383,0.00005508745,0.00004114613,0.00007275288,0.00002628199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009735652,0.00006271074,0.00402566,0.0001403432,0.00008268452,0.0001221019,0.0001443746,0.7235158,0.0104052,0.0303968,0.002620194,0.2283868],"study_design_scores_gemma":[0.000001781927,0.00001006118,0.000134062,0.000004215511,0.000003699193,0.000009207172,0.000008689752,0.9934582,0.0007440497,0.005258514,0.0003633972,0.000004057963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1148179,0.002296101,0.8752852,0.001133763,0.00009423892,0.000036099,0.000117282,0.000800681,0.005418704],"genre_scores_gemma":[0.8131574,0.001345144,0.1790334,0.0003409623,0.0001012413,0.00006243475,0.00029194,0.00008599488,0.005581496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004596679,"threshold_uncertainty_score":0.009139836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05126473338752103,"score_gpt":0.2620400992997386,"score_spread":0.2107753659122176,"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."}}