{"id":"W2125361963","doi":"10.1109/igarss.2007.4423206","title":"Hybrid change detection for watershed impervious surface using multi-time remotely sensed data","year":2007,"lang":"en","type":"article","venue":"","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Calgary","keywords":"Change detection; Impervious surface; Watershed; Remote sensing; Computer science; Decision tree; Random forest; Data mining; Environmental science; Artificial intelligence; Geology; Machine learning","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":[0.0007197483,0.0004531306,0.000533035,0.002926532,0.0002820085,0.0006660307,0.0005370672,0.0004278359,0.0005665114],"category_scores_gemma":[0.00111224,0.0002136919,0.0005568747,0.001011277,0.0002284923,0.001235959,0.0003575103,0.0002982791,0.0001739757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004356327,"about_ca_system_score_gemma":0.0003228447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003169355,"about_ca_topic_score_gemma":0.006454569,"domain_scores_codex":[0.9995593,0.00008294582,0.00003422237,0.0001214681,0.0001580232,0.00004407194],"domain_scores_gemma":[0.9994794,0.0001741911,0.00008952696,0.00005826253,0.0001692122,0.00002958504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000494737,0.0005832464,0.1227784,0.0002114134,0.0003264341,0.0003399389,0.0003925991,0.07817353,0.07482426,0.001683657,0.001067475,0.7191243],"study_design_scores_gemma":[0.0000270494,0.0001910611,0.06161108,0.0000154759,0.00009093425,0.0002233006,0.0002170306,0.9084985,0.02631168,0.00114282,0.001619103,0.00005198041],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5252213,0.0002316238,0.4712614,0.0001232337,0.00004852946,0.0001131958,0.0003651228,0.001343997,0.00129153],"genre_scores_gemma":[0.8352714,0.00007425613,0.1635031,0.00002724425,0.00002063214,0.00006686038,0.0004327763,0.00002868702,0.0005750486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003169355,"threshold_uncertainty_score":0.00630182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08366964120733207,"score_gpt":0.2821715090016202,"score_spread":0.1985018677942881,"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."}}