{"id":"W7071771001","doi":"","title":"Understanding patterns of outdoor recreation in Canadian parks using volunteered geographic information and big data","year":2024,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volunteered geographic information; Recreation; Big data; Citizen science; Proxy (statistics); Public participation GIS; Geographic information system; Population","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":[0.000594095,0.0002029399,0.000189832,0.002184776,0.00129826,0.001938143,0.0005641007,0.0001898611,0.001206387],"category_scores_gemma":[0.003720593,0.0001662303,0.0003253406,0.007187671,0.0005635862,0.0008521346,0.0009888295,0.0005002746,0.0001701881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01056676,"about_ca_system_score_gemma":0.01158854,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9889811,"about_ca_topic_score_gemma":0.9953895,"domain_scores_codex":[0.9994692,0.00007317133,0.00002088701,0.00009244441,0.0001945691,0.0001497333],"domain_scores_gemma":[0.998391,0.0003064957,0.0002571555,0.0001157156,0.0006929539,0.0002367207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004483195,0.00002912301,0.9353465,0.00009827112,0.0001167751,0.0001175802,0.004131695,0.005299306,0.0003518157,0.002604549,0.01016014,0.04169929],"study_design_scores_gemma":[0.000004166863,0.00001168921,0.9600536,0.0001211711,0.00003942366,0.00004470351,0.0109699,0.01117557,0.0002299857,0.0007668325,0.01655095,0.00003213529],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649388,0.0006093356,0.002753954,0.001416093,0.00001827934,0.00005381869,0.02029756,0.00007521086,0.009836842],"genre_scores_gemma":[0.9885931,0.0004874203,0.002529951,0.00007638787,0.000004376376,0.00002062177,0.00687707,0.00002000229,0.00139107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01101887,"threshold_uncertainty_score":0.07666755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3125787976716317,"score_gpt":0.3516027178670546,"score_spread":0.03902392019542295,"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."}}