{"id":"W4394069937","doi":"10.6084/m9.figshare.21424944","title":"Harmonizing and Extending Fragmented 100 Year Flood Hazard Maps in Canada’s Capital Region using Random Forest Classification : Flood susceptibility map generated using ML random forest model.","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Flood myth; Hazard; Geography; Cartography; Forestry; Computer science; Archaeology; Ecology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001101046,0.001931375,0.0009367642,0.00445916,0.00166385,0.002134864,0.003135906,0.001588255,0.01784939],"category_scores_gemma":[0.005148923,0.0007695059,0.00130039,0.007421955,0.0006384526,0.0008744068,0.001783193,0.001590028,0.01187654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007301488,"about_ca_system_score_gemma":0.01229655,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9108816,"about_ca_topic_score_gemma":0.9613506,"domain_scores_codex":[0.9991879,0.00006967423,0.00005403687,0.0002443186,0.0002474707,0.0001964981],"domain_scores_gemma":[0.9972821,0.0004135095,0.0001107404,0.0004369862,0.001479728,0.0002768366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007642809,0.00004244414,0.005443907,0.0003663632,0.00008411565,0.00006993632,0.0001235104,0.002493326,0.0001874288,0.0006628182,0.9844477,0.006002055],"study_design_scores_gemma":[0.0004704657,0.00002219745,0.05498924,0.0006512942,0.0001124011,0.0001302285,0.0006236144,0.01104654,0.001066647,0.002299,0.9284387,0.0001496819],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00121689,0.00006995148,0.0002001634,0.00008554204,0.00002432646,0.00002230106,0.9968429,0.0006860706,0.0008519396],"genre_scores_gemma":[0.001824278,0.00003949548,0.0008631808,0.00002089573,0.000003188977,0.00004348263,0.9965822,0.00009110817,0.0005321883],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08911836,"threshold_uncertainty_score":0.1792864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05588135914757923,"score_gpt":0.2430246019565511,"score_spread":0.1871432428089719,"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."}}