{"id":"W6926166226","doi":"10.25318/1810022901-eng","title":"Computer peripheral price indexes (2002=100)","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Marine Sponges and Natural Products","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Peripheral; Feature (linguistics); Type (biology)","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":[0.0005440452,0.001257184,0.001004604,0.004877595,0.0004387625,0.00261947,0.00138757,0.0009725328,0.1543204],"category_scores_gemma":[0.006245653,0.0006176229,0.0007405967,0.01743707,0.0002263398,0.001295212,0.0007664976,0.001282748,0.1800211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001797839,"about_ca_system_score_gemma":0.002407416,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05453854,"about_ca_topic_score_gemma":0.06185621,"domain_scores_codex":[0.9989826,0.00008994928,0.0001580322,0.0003185122,0.0003066486,0.0001442139],"domain_scores_gemma":[0.9969613,0.0006196577,0.0003906256,0.0005675047,0.001239528,0.0002212471],"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":[0.0000400057,0.000012688,0.001046069,0.0002067613,0.00001546388,0.000009070388,0.000007149475,0.0001888396,0.00004016136,0.0003226337,0.9957795,0.002331681],"study_design_scores_gemma":[0.0001939612,0.00001173301,0.00891041,0.0001405632,0.0000172269,0.00003382523,0.00004357829,0.0003482709,0.00021212,0.0007055605,0.9893638,0.00001892798],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001141039,0.00002447547,0.00001735839,0.00002290781,0.000009084123,0.000003524973,0.998716,0.0001012229,0.0009912883],"genre_scores_gemma":[0.0005164939,0.00005538975,0.0001011079,0.00002831572,0.000007899571,0.00002497197,0.9978309,0.00004803496,0.001386946],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9454615,"threshold_uncertainty_score":0.5162532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004962812206305609,"score_gpt":0.2474355377991031,"score_spread":0.2424727255927975,"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."}}