{"id":"W4322492003","doi":"10.32920/22183756","title":"Lake Watershed Tourists: Who They Are and How to Attract Them","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Diverse Aspects of Tourism Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Sustainability; Tourism; Demographics; Destinations; Marketing; Business; Geography; Watershed; Ecology; Sociology; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006191898,0.0001695108,0.0001670099,0.0004134976,0.001886361,0.002843742,0.0002368958,0.0007338595,0.01488413],"category_scores_gemma":[0.001804371,0.0001392294,0.0001458969,0.0005635636,0.000585519,0.001617099,0.001466973,0.0007568315,0.002562528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001061105,"about_ca_system_score_gemma":0.002219258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02239542,"about_ca_topic_score_gemma":0.07017713,"domain_scores_codex":[0.9997105,0.00008055683,0.000009305886,0.00002089715,0.00006354636,0.0001151728],"domain_scores_gemma":[0.9992419,0.00009496119,0.00007805248,0.00001316294,0.000107955,0.0004639699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001601871,0.0002929719,0.3662625,0.0008731845,0.00005194034,0.001628523,0.06967691,0.0001296948,0.002497357,0.007263069,0.1981838,0.35298],"study_design_scores_gemma":[0.00002325903,0.0002735463,0.386373,0.0008479469,0.0000456353,0.0009582508,0.3298191,0.0004045434,0.0004406224,0.002162336,0.2785765,0.00007536831],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8576888,0.005604497,0.0009180431,0.06979255,0.0003748201,0.0002304743,0.0007800097,0.0001267816,0.064484],"genre_scores_gemma":[0.9268814,0.007890742,0.002101885,0.00707818,0.0001818595,0.0002053846,0.0006342032,0.00005828354,0.05496803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02239542,"threshold_uncertainty_score":0.04979235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1135581653106727,"score_gpt":0.352065839513002,"score_spread":0.2385076742023292,"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."}}