{"id":"W2060580088","doi":"10.1002/hyp.1235","title":"Development of a historical ice database for the study of climate change in Canada","year":2002,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Center for Northern Studies","funders":"University of Waterloo","keywords":"Database; Proxy (statistics); Sea ice; Climate change; Climatology; Arctic ice pack; Cryosphere; Environmental science; Government (linguistics); Snow; Physical geography; Geography; Meteorology; Oceanography; Geology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00189866,0.0004551887,0.0005037642,0.01256514,0.00198019,0.002170166,0.001759279,0.0002800104,0.003460987],"category_scores_gemma":[0.005749762,0.0003052534,0.0003496639,0.02203863,0.0003039643,0.0008994494,0.000855844,0.0007195594,0.00114914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02220447,"about_ca_system_score_gemma":0.04457645,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9825292,"about_ca_topic_score_gemma":0.98349,"domain_scores_codex":[0.9984106,0.00007906818,0.0001919244,0.0002064735,0.0009123403,0.000199617],"domain_scores_gemma":[0.9852237,0.0003549232,0.0005547099,0.0006511159,0.0123125,0.0009031298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002694536,0.0002319399,0.4304758,0.0008113338,0.0002252533,0.0004785481,0.001330354,0.01317018,0.002285999,0.005852008,0.2359463,0.3089228],"study_design_scores_gemma":[0.00005130286,0.00004123964,0.6649064,0.0003679686,0.0001121485,0.0001324255,0.0014495,0.01778772,0.002379265,0.0006475046,0.3120162,0.0001083145],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.09459885,0.001549858,0.01547936,0.0005836484,0.0001742516,0.001764814,0.8596994,0.001283889,0.02486602],"genre_scores_gemma":[0.2471719,0.00209098,0.04795038,0.0001794105,0.00004744648,0.00138097,0.6943244,0.0002577121,0.006596819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02220447,"threshold_uncertainty_score":0.1611055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06249235853446066,"score_gpt":0.2323127226088509,"score_spread":0.1698203640743903,"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."}}