{"id":"W6901606589","doi":"10.6068/dp14ba811b05210","title":"Trend 1998 - 2004. Statistics Canada. CANSIM: Business, Consumer and Property Services - Accommodation and Food | Country: Canada | Table: Canadian travel survey, travel characteristics, by province visited | Variable: Type of accommodation, resort, Total visits | Units: # Person-trips x 1,000, 1998-2004. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-009.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tourism; Accommodation; Census; Official statistics; Economic statistics; Hospitality; Summary statistics; Descriptive 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":[0.002371755,0.002665494,0.002767523,0.009291946,0.003473819,0.004908355,0.005400621,0.001446632,0.09422911],"category_scores_gemma":[0.01999277,0.001854144,0.002207251,0.04415645,0.0006445638,0.002746051,0.00225167,0.003241251,0.05640843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05302069,"about_ca_system_score_gemma":0.1428578,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9955362,"about_ca_topic_score_gemma":0.9937314,"domain_scores_codex":[0.9955266,0.0003017029,0.0004957101,0.0005767759,0.002067875,0.001031286],"domain_scores_gemma":[0.9656488,0.001156659,0.0009663709,0.001001274,0.02961674,0.001610048],"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":[0.00002412108,0.000005695217,0.0009419666,0.0002528891,0.0000197438,0.000007199209,0.00002329414,0.00009922444,0.000009052128,0.0003426047,0.9965851,0.001689091],"study_design_scores_gemma":[0.000144028,0.0000124251,0.02316703,0.0009077691,0.00008007479,0.00002839354,0.0004899903,0.0004634406,0.000149025,0.0005921275,0.9738845,0.00008117854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005154393,0.00006193192,0.00002892376,0.000134506,0.00003453467,0.0000160454,0.9986814,0.0000635543,0.0009275138],"genre_scores_gemma":[0.000874091,0.000356246,0.0004439574,0.0001658675,0.00002086114,0.0001365855,0.9933617,0.0001313198,0.004509388],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09422911,"threshold_uncertainty_score":0.3846938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02822830041059952,"score_gpt":0.2310666354044613,"score_spread":0.2028383349938618,"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."}}