{"id":"W2547282450","doi":"10.1109/igarss.2016.7729868","title":"Morphological interpolation for temporal changes","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; Indian Statistical Institute","keywords":"Interpolation (computer graphics); Focus (optics); Mathematical morphology; Computer science; Linear interpolation; Remote sensing; Artificial intelligence; Image (mathematics); Geology; Image processing; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001207825,0.0006119244,0.0005489698,0.001363414,0.0005021887,0.001326548,0.0008957018,0.0008945854,0.003862866],"category_scores_gemma":[0.003093639,0.0003347343,0.001437597,0.001797505,0.001333434,0.001777393,0.001232853,0.001835112,0.001157171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004507519,"about_ca_system_score_gemma":0.0005557183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007900331,"about_ca_topic_score_gemma":0.0007361773,"domain_scores_codex":[0.9995596,0.0001016445,0.00003195372,0.0001180702,0.0001535936,0.00003520726],"domain_scores_gemma":[0.9990951,0.0003824152,0.0001257494,0.0001907063,0.000170671,0.00003535226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001450231,0.00004439966,0.0008421426,0.0004565664,0.00009406761,0.0005829892,0.0003818499,0.1079468,0.03178966,0.5916826,0.004519512,0.2615145],"study_design_scores_gemma":[0.00002772214,0.0001572953,0.001558396,0.00008724761,0.0000620265,0.001074717,0.0001467938,0.6545697,0.01238835,0.2573073,0.07255778,0.00006264511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004328528,0.0008730339,0.9911172,0.0002182322,0.0001386005,0.00002583558,0.00007748417,0.0002126354,0.00300835],"genre_scores_gemma":[0.1745189,0.00397659,0.8064993,0.0002506034,0.0005024829,0.0001569118,0.0004904731,0.0002988028,0.01330584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003862866,"threshold_uncertainty_score":0.01292253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04334955235887736,"score_gpt":0.3200270488475128,"score_spread":0.2766774964886354,"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."}}