{"id":"W2089586661","doi":"10.1504/ijram.2011.042673","title":"Assessing global change when data are sparse","year":2011,"lang":"en","type":"article","venue":"International Journal of Risk Assessment and Management","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Natural hazard; Climate change; Hazard; Global warming; Inference; Statistical inference; Computer science; Risk analysis (engineering); Econometrics; Geography; Statistics; Business; Meteorology; Economics; Mathematics; Artificial intelligence; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.01513753,0.0006085621,0.001351159,0.005772702,0.0008531721,0.00325605,0.0008935967,0.002446461,0.001688717],"category_scores_gemma":[0.08791138,0.000531686,0.0008656852,0.008121754,0.001645699,0.006779803,0.002858661,0.001405058,0.0002355415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373751,"about_ca_system_score_gemma":0.0009723831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008060524,"about_ca_topic_score_gemma":0.008560897,"domain_scores_codex":[0.9918563,0.003924403,0.0009307448,0.001449818,0.001515483,0.0003232746],"domain_scores_gemma":[0.8938128,0.08664825,0.01044329,0.0049845,0.003244535,0.0008666298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003600798,0.0001305698,0.5506638,0.001355126,0.001301213,0.002077724,0.002628426,0.171242,0.001426571,0.04857966,0.009573029,0.2106619],"study_design_scores_gemma":[0.00006369011,0.0003179177,0.3363931,0.0006095367,0.0004127149,0.001272673,0.006146927,0.2660092,0.001294444,0.3565619,0.03069979,0.0002181319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7094166,0.007765475,0.2210083,0.02202235,0.0006158564,0.000376736,0.01787022,0.0005136279,0.02041099],"genre_scores_gemma":[0.9613365,0.001824217,0.02958485,0.0008689573,0.0003706686,0.0001894997,0.005187735,0.00003104447,0.000606551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01513753,"threshold_uncertainty_score":0.08005583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5194603267570926,"score_gpt":0.5103314068759889,"score_spread":0.009128919881103759,"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."}}