{"id":"W6901643577","doi":"10.6068/dp14ba81c71b038","title":"Trend 2007 - 2011. Statistics Canada. CANSIM: Business, Consumer and Property Services - Accommodation and Food | Country: Canada | Table: Traveller accommodation, operating expenses, by North American Industry Classification System (NAICS) | Variable: Repair and maintenance, Accommodation services | Units: %, 2007-2011. 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":"Official statistics; Accommodation; Census; Tourism; Economic statistics; Summary statistics; Hospitality industry; Hospitality","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":[0.00196279,0.002525835,0.002699697,0.008978587,0.003308993,0.005168967,0.004983814,0.001536001,0.08954051],"category_scores_gemma":[0.01936451,0.001675127,0.00216278,0.04207241,0.0006702007,0.002784107,0.002314063,0.003268103,0.06178347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04717976,"about_ca_system_score_gemma":0.1254372,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9934425,"about_ca_topic_score_gemma":0.9920117,"domain_scores_codex":[0.9958292,0.0002648543,0.0004490906,0.0005768441,0.001907351,0.0009726799],"domain_scores_gemma":[0.9677821,0.00121667,0.0009643928,0.0009926091,0.02754582,0.001498361],"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":[0.00001792058,0.000004808076,0.0008234686,0.0002114854,0.00001733546,0.000006092427,0.00001703309,0.00009105726,0.000007685247,0.000303372,0.9972466,0.001253152],"study_design_scores_gemma":[0.0001279269,0.00001019568,0.01823369,0.0008679118,0.00006796268,0.00002739733,0.0004530905,0.0004705621,0.0001556059,0.0006271542,0.9788801,0.00007839363],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004311069,0.0000527261,0.00002215188,0.0001184198,0.00002757066,0.00001055904,0.9989555,0.0000511507,0.0007188738],"genre_scores_gemma":[0.0006400692,0.0002659019,0.0003047217,0.0001337985,0.00001791321,0.00008801413,0.9954219,0.00009784875,0.003029677],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08954051,"threshold_uncertainty_score":0.3423147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02440604239718298,"score_gpt":0.2338448467127427,"score_spread":0.2094388043155598,"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."}}