{"id":"W2121819232","doi":"10.1109/ccece.2006.277618","title":"Projection Pursuit Feature Analysis for Pan-Sharpened Multispectral Ikonos Imagery","year":2006,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Multispectral image; Panchromatic film; Clutter; Artificial intelligence; Computer science; Hyperspectral imaging; Preprocessor; Computer vision; Projection (relational algebra); Feature (linguistics); Pattern recognition (psychology); Feature extraction; Projection pursuit; Remote sensing; Geography; Radar","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":[0.0003776763,0.0004118532,0.0003304613,0.000502867,0.0001285199,0.0002844767,0.00018146,0.0001661843,0.0004143817],"category_scores_gemma":[0.001105214,0.0001301988,0.0003351034,0.0006011061,0.0002378747,0.000367768,0.0003133944,0.000373883,0.0001499835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001511013,"about_ca_system_score_gemma":0.0002373665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083301,"about_ca_topic_score_gemma":0.0008732524,"domain_scores_codex":[0.9998659,0.00003657543,0.000007317877,0.00002239465,0.00005624185,0.00001156693],"domain_scores_gemma":[0.9997596,0.0001442474,0.00003172667,0.0000193793,0.00003716876,0.000007838932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003925172,0.0001296543,0.003902777,0.0002228359,0.00008483128,0.0002586933,0.0001994872,0.1992715,0.236173,0.006670217,0.001030794,0.5516636],"study_design_scores_gemma":[0.000007985042,0.00007391885,0.005146887,0.0000037364,0.000009879402,0.00008505023,0.00002889662,0.9714358,0.02084911,0.00188146,0.0004642326,0.00001290005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1905556,0.0001231327,0.8079985,0.00007658821,0.000006370557,0.00002646212,0.00009775726,0.0003142726,0.0008013183],"genre_scores_gemma":[0.6621999,0.0002553863,0.3360994,0.00002046842,0.00001255476,0.00006645937,0.0004066319,0.0000523893,0.0008868182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001083301,"threshold_uncertainty_score":0.002153993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0119669181737782,"score_gpt":0.2283508240269702,"score_spread":0.216383905853192,"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."}}