{"id":"W2065866494","doi":"10.1080/07038992.2000.10874763","title":"Monitoring Wetlands Inundation Patterns using RADARSAT Multitemporal Data","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Forestry; Geography; Physical geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"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":[0.0002691092,0.0001461687,0.0001407154,0.001235554,0.0001467396,0.0002823082,0.0001605499,0.0001306063,0.0004543874],"category_scores_gemma":[0.0004916113,0.0001139482,0.0001088691,0.0008603967,0.00008258957,0.0002554206,0.0001492691,0.00009394582,0.0001047283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003123769,"about_ca_system_score_gemma":0.0002688612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01968193,"about_ca_topic_score_gemma":0.04060675,"domain_scores_codex":[0.9998854,0.00001825081,0.000009187805,0.00002688979,0.000041286,0.00001900396],"domain_scores_gemma":[0.9996468,0.00006566103,0.0001046083,0.00002917066,0.0001231648,0.00003048901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002862462,0.0001773252,0.6982566,0.000124928,0.0001073816,0.0001970743,0.0004050778,0.01787804,0.09636463,0.0001555564,0.0009742745,0.1850728],"study_design_scores_gemma":[0.00001600147,0.00009615918,0.960385,0.00001052143,0.00004687546,0.00009317037,0.0001958871,0.02410881,0.01339204,0.00004914624,0.001593929,0.00001247466],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961531,0.00007048769,0.001954853,0.0000225304,0.000002487549,0.00001923176,0.0009891377,0.0001132863,0.0006749812],"genre_scores_gemma":[0.9873397,0.0001034454,0.009582062,0.00001248244,0.000005611992,0.00003004655,0.002108213,0.000008881709,0.0008094265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01968193,"threshold_uncertainty_score":0.03913474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03949990004204493,"score_gpt":0.2702639591605187,"score_spread":0.2307640591184737,"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."}}