{"id":"W2090608996","doi":"10.1145/1864349.1864354","title":"Identifying the activities supported by locations with community-authored content","year":2010,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Computer science; Context (archaeology); Set (abstract data type); World Wide Web; Scale (ratio); Service (business); Process (computing); Data science; Geography; Artificial intelligence; Cartography; Business; Marketing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005451261,0.0001089325,0.00009933388,0.00004261987,0.0009787674,0.0005039034,0.0007364217,0.00004077014,0.00002028333],"category_scores_gemma":[0.00002636064,0.00006548094,0.00003250647,0.0002069587,0.0001832485,0.0003442915,0.0001587597,0.0005947584,0.00001361628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001065975,"about_ca_system_score_gemma":0.00005277001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009692811,"about_ca_topic_score_gemma":0.001098806,"domain_scores_codex":[0.999153,0.000143606,0.0001351083,0.0001527655,0.0001979251,0.0002175579],"domain_scores_gemma":[0.9985661,0.0002675552,0.00006874857,0.0009536689,0.00007876728,0.00006515117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001829953,0.000446609,0.006219542,0.00004739198,0.000164149,0.00001390248,0.03140628,0.00006165019,0.7695791,0.1188159,0.02382346,0.04940378],"study_design_scores_gemma":[0.002188355,0.0004092763,0.02994321,0.0001993876,0.0001066519,0.0007214166,0.05642684,0.07552426,0.8002077,0.004368079,0.02829588,0.001608987],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5916771,0.00001288536,0.4034876,0.001441743,0.0001895106,0.0001093059,0.000001138766,0.0002067654,0.002873935],"genre_scores_gemma":[0.9899967,0.000001433235,0.007177213,0.0003990037,0.00001926168,0.00001643,0.000006808954,0.000008671556,0.002374489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3983196,"threshold_uncertainty_score":0.7527987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03779842234980663,"score_gpt":0.2528363811212154,"score_spread":0.2150379587714088,"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."}}