{"id":"W2161526633","doi":"10.1007/s10584-012-0476-7","title":"Time series data for Canadian arctic vertebrates: IPY contributions to science, management, and policy","year":2012,"lang":"en","type":"article","venue":"Climatic Change","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Carleton University; Université du Québec à Rimouski; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; Canada Research Chairs; University of Alberta; ArcticNet; University of Ottawa","keywords":"Context (archaeology); Arctic; Population; Geography; Ecology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004925653,0.0001038226,0.000130668,0.00008402856,0.0004771762,0.00004827435,0.0004032644,0.0000180781,0.0008127307],"category_scores_gemma":[0.0002855416,0.00009478912,0.00001328458,0.0005266414,0.0002778796,0.0007149351,0.001205235,0.0000285392,0.0007830887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005132796,"about_ca_system_score_gemma":0.00002133711,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05157888,"about_ca_topic_score_gemma":0.1089458,"domain_scores_codex":[0.9988525,0.00001136006,0.0001167557,0.0002481543,0.000145489,0.0006257428],"domain_scores_gemma":[0.9990956,0.00002985979,0.00003038922,0.0004198427,0.00001470418,0.0004096027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000714165,0.0005314616,0.4505212,0.001792704,0.0002566609,0.00001524028,0.01195638,0.000003361018,0.0004719648,0.1001341,0.2850144,0.1492311],"study_design_scores_gemma":[0.0003194707,0.0001028325,0.4731815,0.00005485321,0.00007205162,0.00001058527,0.0004409147,0.0010451,0.00001414953,0.0009777448,0.5234832,0.0002975995],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5387181,0.003081444,0.0006162493,0.09049246,0.001265255,0.01787988,0.008207596,0.0003429524,0.3393961],"genre_scores_gemma":[0.988748,0.0002459261,0.00277502,0.005551072,0.0002867878,0.0004106041,0.0001046225,0.00002010981,0.001857923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4500299,"threshold_uncertainty_score":0.9999949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06225934898863709,"score_gpt":0.3174495842250493,"score_spread":0.2551902352364122,"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."}}