{"id":"W3100853521","doi":"10.1080/19479832.2020.1845244","title":"A context-driven pansharpening method using superpixel based texture analysis","year":2020,"lang":"en","type":"article","venue":"International Journal of Image and Data Fusion","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Weighting; Artificial intelligence; Pattern recognition (psychology); Pyramid (geometry); Context (archaeology); Cluster analysis; Image resolution; Image (mathematics); Mathematics","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":[0.0003199888,0.0008175934,0.000788619,0.001557182,0.0002533025,0.0005788016,0.001004575,0.0005620198,0.001697074],"category_scores_gemma":[0.0007274901,0.0004532,0.0007246431,0.001139582,0.0003440808,0.001116225,0.0007139253,0.0009605089,0.0006649823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002557074,"about_ca_system_score_gemma":0.0004620606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001762503,"about_ca_topic_score_gemma":0.003468129,"domain_scores_codex":[0.9996914,0.00002774428,0.00001253886,0.00008434669,0.0001501632,0.00003384446],"domain_scores_gemma":[0.9997104,0.00005007604,0.00004463703,0.0000749149,0.0001011956,0.00001875085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002623293,0.00008072796,0.0007635283,0.000248429,0.00009656465,0.0001318605,0.0001209374,0.04367651,0.2808202,0.002691023,0.001685892,0.6694219],"study_design_scores_gemma":[0.00003357298,0.0001648758,0.003478843,0.00002546155,0.0001148587,0.0004103306,0.00006148046,0.8830774,0.102066,0.002433341,0.008078194,0.00005557995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01574766,0.0003405701,0.9822433,0.00004472278,0.00003593689,0.00005864335,0.00006060416,0.0007935558,0.0006750617],"genre_scores_gemma":[0.1522502,0.000576939,0.8444465,0.0001310301,0.00008104652,0.00008446485,0.0003456845,0.0002809267,0.001803193],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001762503,"threshold_uncertainty_score":0.005677223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04092120648154868,"score_gpt":0.3476057484989616,"score_spread":0.3066845420174129,"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."}}