{"id":"W1717587608","doi":"10.16995/dscn.114","title":"‘Buried Beneath the Waves’: Using GIS to Examine the Physical and Social Impact of a Historical Flood","year":2009,"lang":"en","type":"article","venue":"Digital Studies / Le champ numérique","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Flood myth; Natural disaster; Geographic information system; Natural (archaeology); Environmental planning; Geography; Environmental resource management; History; Cartography; Archaeology; Meteorology; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001126974,0.0002195873,0.0001397269,0.00254947,0.0009108646,0.002099304,0.0002917228,0.0004059426,0.0009480855],"category_scores_gemma":[0.003366187,0.0001441838,0.0001493968,0.003344202,0.001825992,0.00271548,0.0009632091,0.0003385794,0.0001425174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001166425,"about_ca_system_score_gemma":0.0008053224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03693067,"about_ca_topic_score_gemma":0.05933938,"domain_scores_codex":[0.9994813,0.0003553349,0.00001655072,0.0000325896,0.00007996862,0.00003434491],"domain_scores_gemma":[0.9990456,0.0006227851,0.0001330942,0.00006443662,0.00008937519,0.00004469593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002871687,0.000225154,0.4538609,0.0003127011,0.0001946888,0.002415672,0.1985096,0.01138513,0.00379398,0.03982934,0.009247871,0.2799378],"study_design_scores_gemma":[0.00004443147,0.0004246904,0.34562,0.0003562671,0.0001173644,0.001510111,0.4922185,0.04061858,0.003548823,0.03875887,0.07662988,0.0001524979],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971514,0.0004904897,0.008149534,0.001271833,0.00002371235,0.00005759158,0.000428332,0.0001121767,0.01795222],"genre_scores_gemma":[0.9785621,0.0005037923,0.01919905,0.00008574819,0.00001292919,0.00003495231,0.0002770761,0.00001777551,0.001306475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03693067,"threshold_uncertainty_score":0.07343143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0264186060125629,"score_gpt":0.2955787336976662,"score_spread":0.2691601276851033,"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."}}