{"id":"W6963986617","doi":"10.25318/1610005901-eng","title":"Primary production of iron and steel and net shipments of steel shapes to consuming industries","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Production (economics); Service (business); Truck; Finished good","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004867592,0.001572626,0.001137745,0.005043189,0.0009047666,0.001890621,0.001995866,0.001014399,0.04634532],"category_scores_gemma":[0.004478382,0.0005470041,0.000970957,0.01520647,0.0003776817,0.0009065247,0.0008934477,0.001722443,0.05469675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005156381,"about_ca_system_score_gemma":0.008594878,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5554876,"about_ca_topic_score_gemma":0.602497,"domain_scores_codex":[0.9990535,0.00006008752,0.0001054336,0.0002521715,0.00031214,0.0002165978],"domain_scores_gemma":[0.9965652,0.0004197028,0.0003467909,0.000414009,0.001958162,0.0002961673],"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":[0.00003699557,0.00001551174,0.002513438,0.0002104891,0.00002001827,0.00001539441,0.00001676191,0.0002431024,0.00004221168,0.000359749,0.9948581,0.001668264],"study_design_scores_gemma":[0.0001150362,0.00001177286,0.02666106,0.0002335188,0.00003117827,0.00004771306,0.0001887747,0.0004912373,0.0002602297,0.0004326887,0.9714955,0.00003129403],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002081917,0.00003078669,0.00001561078,0.00002743437,0.00001367316,0.000003495481,0.9991112,0.00003904404,0.0005504927],"genre_scores_gemma":[0.0005797726,0.00004898134,0.00006625291,0.00001485727,0.000004312205,0.00001728154,0.9981641,0.00001458405,0.001089904],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4445124,"threshold_uncertainty_score":0.8942605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01354058853959792,"score_gpt":0.2640974775830159,"score_spread":0.250556889043418,"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."}}