{"id":"W4285464600","doi":"10.32920/ryerson.14669106","title":"Design With Nature 2.0 – A Geodata Infrastructure Approach to Map Overlay","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Geospatial analysis; Thematic map; Overlay; Computer science; Hazard map; Spatial data infrastructure; Geographic information system; Vector map; Data science; Information system; World Wide Web; Cartography; Information retrieval; Geography; Hazard; Spatial analysis; Engineering; Remote sensing; 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":[0.005758109,0.0006296518,0.0003435626,0.001153063,0.00106512,0.003563493,0.002000593,0.0008992073,0.006569559],"category_scores_gemma":[0.01106747,0.0007446599,0.0008495224,0.001286323,0.002144735,0.00424545,0.003759987,0.001336736,0.00124137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001690884,"about_ca_system_score_gemma":0.002315668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003511043,"about_ca_topic_score_gemma":0.005050516,"domain_scores_codex":[0.9955178,0.002336979,0.0002668453,0.0004680357,0.00119678,0.0002134938],"domain_scores_gemma":[0.9945765,0.001781569,0.0003266263,0.00183526,0.001108388,0.0003716662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002808151,0.0001982543,0.004196228,0.0005537262,0.0001380292,0.0005525789,0.007229233,0.05624645,0.01654971,0.6677301,0.01597206,0.2303528],"study_design_scores_gemma":[0.0001449035,0.0003284015,0.001989504,0.0001822953,0.0001137419,0.0006149673,0.001752977,0.2210048,0.02002485,0.3272222,0.4264975,0.0001238815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005219928,0.000037519,0.9812986,0.0005349064,0.00004700232,0.0002109936,0.0001047074,0.001738063,0.01080839],"genre_scores_gemma":[0.1338203,0.0001088377,0.8578052,0.0001857138,0.00001673124,0.0006441912,0.0002168839,0.0006157952,0.006586384],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006569559,"threshold_uncertainty_score":0.03045213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0215494646201291,"score_gpt":0.2777617887494703,"score_spread":0.2562123241293413,"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."}}