{"id":"W6920638149","doi":"10.6068/dp14ba85648d952","title":"Trend 1994 - 2003. Statistics Canada. CANSIM: Business, Consumer and Property Services - Arts, Entertainment and Recreation | Country: Canada | Table: Summary profile of heritage institutions and nature parks | Variable: Other operating expenses, Other type of institutions | Units: , 1994-2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-010.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recreation; Entertainment; Amusement; Census; Socioeconomic status; Descriptive statistics; Tourism; Official statistics","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":[0.002169427,0.002519903,0.002693978,0.009767116,0.003435764,0.005277575,0.005665461,0.001478127,0.09299683],"category_scores_gemma":[0.01837753,0.001715298,0.001930374,0.04647819,0.0006968467,0.002783702,0.002243676,0.003164929,0.05627917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05860753,"about_ca_system_score_gemma":0.1599477,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.995822,"about_ca_topic_score_gemma":0.9941923,"domain_scores_codex":[0.9950449,0.0002942644,0.0004976533,0.0006405677,0.00235721,0.00116544],"domain_scores_gemma":[0.9610309,0.001335828,0.001167434,0.001035602,0.03358229,0.001847939],"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":[0.000021015,0.000006007436,0.001011061,0.0001904089,0.00001809567,0.000006370723,0.00002073945,0.0001041201,0.000009040878,0.0003722815,0.9968598,0.001381032],"study_design_scores_gemma":[0.0001306684,0.00001131486,0.02386225,0.0007726504,0.00006785693,0.00002569066,0.0005443332,0.0005136146,0.0001801589,0.0006446147,0.9731663,0.00008057995],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005928484,0.00004783921,0.00002649289,0.0001417092,0.00002640368,0.00001462003,0.9986128,0.00006172172,0.001009013],"genre_scores_gemma":[0.0009367959,0.0002880144,0.0004000575,0.0001733679,0.00001921126,0.0001226708,0.9928827,0.0001386457,0.005038498],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09299683,"threshold_uncertainty_score":0.4252294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03265816881818165,"score_gpt":0.26381150122118,"score_spread":0.2311533324029983,"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."}}