{"id":"W6931853320","doi":"10.5683/sp3/z6oouw","title":"Accessing Canada's Open Data Through APIs","year":2017,"lang":"en","type":"dataset","venue":"Borealis","topic":"Pharmaceutical Quality and Counterfeiting","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Key (lock); Data collection; Focus (optics); Field (mathematics); Software","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005771088,0.000289538,0.0006646523,0.00002615197,0.0003799499,0.0005442707,0.004257384,0.0002281572,0.0003608014],"category_scores_gemma":[0.0009179061,0.0002515648,0.00004465682,0.00004720503,0.0001177046,0.0005413945,0.003593051,0.0006982582,0.00001477643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001865893,"about_ca_system_score_gemma":0.002787413,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934387,"about_ca_topic_score_gemma":0.9787123,"domain_scores_codex":[0.9978595,0.00008554083,0.0004040931,0.0006503201,0.0005879158,0.0004125785],"domain_scores_gemma":[0.9953216,0.0001259107,0.0003515379,0.003877127,0.00009260269,0.0002312386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007272207,0.00005217508,0.00001282927,0.0004236314,0.0001428983,0.0004868599,0.00001232987,3.903182e-8,0.000002323497,0.00001211748,0.9946553,0.004126813],"study_design_scores_gemma":[0.0006449792,0.0000190481,0.0001690783,0.0007859708,0.0004843536,0.00006489958,0.00004032756,0.00002591802,0.00003437547,0.00007405547,0.9973723,0.0002847049],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004442413,0.0004352533,0.00000922631,0.004586592,0.0002621802,0.0003904249,0.9840032,0.00001963376,0.01028909],"genre_scores_gemma":[0.00007612439,0.0004081528,0.0001214586,0.01074264,0.0009229688,0.00001619269,0.9874209,0.00002591553,0.0002656729],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01472644,"threshold_uncertainty_score":0.9999937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3308789090378296,"score_gpt":0.505607078745697,"score_spread":0.1747281697078674,"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."}}