{"id":"W1987544383","doi":"10.5539/cis.v8n1p128","title":"The Structural Characteristics of Tourism Economic Network in Xinjiang Province","year":2015,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tourism; Computer science; Gravity model of trade; Network structure; Economic geography; Enhanced Data Rates for GSM Evolution; Social network (sociolinguistics); Regional science; Business; Geography; Telecommunications; Social media; World Wide Web; International trade","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0002197716,0.00013558,0.0001291234,0.002377629,0.0004651483,0.00044076,0.0002446565,0.0001304179,0.001981842],"category_scores_gemma":[0.0008403595,0.0001146822,0.0001547623,0.003632668,0.0002049369,0.0004157176,0.0003958134,0.00008909197,0.0001268042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048233,"about_ca_system_score_gemma":0.0006146526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04746836,"about_ca_topic_score_gemma":0.05585523,"domain_scores_codex":[0.9998333,0.0000234518,0.00002389538,0.00003890428,0.00003865523,0.00004179962],"domain_scores_gemma":[0.9995598,0.00009876167,0.0001432598,0.00002806072,0.0001017247,0.00006835791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006175778,0.00002463146,0.9826435,0.00004664603,0.00002279605,0.0001973352,0.0007900153,0.004631632,0.0005598668,0.0009206187,0.0007315322,0.009369602],"study_design_scores_gemma":[0.000002605281,0.00001928429,0.9914351,0.000009640567,0.0000117936,0.00007182036,0.001016754,0.006239365,0.0001254824,0.0001511458,0.0009128784,0.000004038776],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979218,0.00003252556,0.0001460735,0.00002211286,0.000001427676,0.000007368194,0.001082927,0.000004587283,0.000781222],"genre_scores_gemma":[0.9979926,0.00005181009,0.0001697144,0.000002459055,0.000001316604,0.00001246942,0.001423404,8.291079e-7,0.0003454129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04746836,"threshold_uncertainty_score":0.09438413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0191986674738023,"score_gpt":0.2981024855911868,"score_spread":0.2789038181173845,"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."}}