{"id":"W2399833365","doi":"","title":"Open Data in Vancouver: The Inspiration and the Vision.","year":2011,"lang":"en","type":"article","venue":"IASSIST Conference","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Computer graphics (images); Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["open_science"],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"commentary","about_ca_system":false,"about_ca_topic":true,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.008936161,0.0000737896,0.0001504487,0.00004508943,0.0001967804,0.0009941686,0.005585962,0.00002471199,0.0004939869],"category_scores_gemma":[0.001912082,0.00003203024,0.00001301181,0.0002846973,0.0003896581,0.001268831,0.005540699,0.0001049476,0.0001122583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007240135,"about_ca_system_score_gemma":0.00006624896,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003134873,"about_ca_topic_score_gemma":0.04711359,"domain_scores_codex":[0.9980541,0.0005254716,0.0003815112,0.0004202533,0.0005025723,0.0001161025],"domain_scores_gemma":[0.996934,0.0007838681,0.0001638256,0.002005373,0.00008131842,0.00003162479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001583481,0.00008127082,0.002853455,0.000003332183,0.00001141944,0.000004526094,0.004233819,0.00000102984,0.000003871146,0.4839709,0.09841421,0.4102638],"study_design_scores_gemma":[0.001376338,0.00004496444,0.1972538,0.00002802412,0.00001637364,0.000001148406,0.006971279,0.005623191,0.00001653624,0.2117173,0.5768039,0.0001471077],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04901808,0.0004449695,0.07548285,0.04728276,0.00231224,0.003452975,0.0003522501,0.00006885009,0.821585],"genre_scores_gemma":[0.9957721,0.00004157582,0.0005200066,0.001854939,0.00001465321,0.00002404081,0.000009423962,0.000002343997,0.001760953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.946754,"threshold_uncertainty_score":0.9997943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.603460426186831,"score_gpt":0.4682804701029587,"score_spread":0.1351799560838723,"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."}}