{"id":"W2122069182","doi":"10.3390/rs71013528","title":"Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Environment and Climate Change Canada","funders":"","keywords":"Remote sensing; Land cover; Operationalization; Environmental science; Random forest; Digital elevation model; Computer science; Geology; Land use; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.006142801,0.001226315,0.0005108178,0.0006525226,0.0003641814,0.000894955,0.0007590641,0.0007547368,0.0005069274],"category_scores_gemma":[0.0112415,0.0003988789,0.0006882125,0.0004886073,0.0003998962,0.001508526,0.0008317247,0.000606072,0.0002378533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006503257,"about_ca_system_score_gemma":0.001135553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01978833,"about_ca_topic_score_gemma":0.0248212,"domain_scores_codex":[0.9985952,0.0007503482,0.00006414168,0.0002595682,0.0002092785,0.0001214438],"domain_scores_gemma":[0.9944205,0.00396168,0.0002728286,0.0004652345,0.0007865803,0.00009333574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008570376,0.0004092868,0.0454478,0.00007888182,0.0002725,0.0000975855,0.000158461,0.8482583,0.01067723,0.0005383361,0.0003535634,0.09285115],"study_design_scores_gemma":[0.00003547388,0.0002135537,0.006807695,0.000009990824,0.00005059664,0.00002606248,0.00005326374,0.9871176,0.005073676,0.0003659896,0.0002179415,0.00002819524],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.857464,0.0001153934,0.1394421,0.0002021614,0.00001947141,0.000196458,0.000318801,0.0008695286,0.001372196],"genre_scores_gemma":[0.9048604,0.00004426935,0.09431747,0.00003608025,0.000005580765,0.0001034653,0.0003300503,0.00004394386,0.000258747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01978833,"threshold_uncertainty_score":0.03934628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0304335230084645,"score_gpt":0.2473916176920462,"score_spread":0.2169580946835817,"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."}}