{"id":"W4403710339","doi":"10.2196/51701","title":"Uncovering the Top Nonadvertising Weight Loss Websites on Google: A Data-Mining Approach","year":2024,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Preprint; Computer science; Advertising; World Wide Web; Business","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":[0.001719808,0.0006966558,0.0008323487,0.02516034,0.0008668734,0.002293143,0.0008596379,0.0006812519,0.0007798423],"category_scores_gemma":[0.008080507,0.0002972043,0.001080032,0.01471912,0.0004992547,0.001834078,0.001269135,0.0007292763,0.0008656539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007709431,"about_ca_system_score_gemma":0.001478322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01121709,"about_ca_topic_score_gemma":0.01400881,"domain_scores_codex":[0.9979199,0.0002757416,0.0003644732,0.0003606552,0.0008089292,0.000270285],"domain_scores_gemma":[0.9918365,0.00359076,0.001960551,0.0003904411,0.001793254,0.0004284678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005415926,0.0006581768,0.8470876,0.003299125,0.0002883495,0.002257189,0.004289845,0.002098413,0.007242822,0.001188269,0.01247761,0.118571],"study_design_scores_gemma":[0.00004620002,0.0003236443,0.9058847,0.0007241508,0.0003769875,0.002749176,0.01315641,0.04654087,0.006939594,0.001931829,0.02122031,0.0001060376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646133,0.001559527,0.002964601,0.0004074582,0.00002526106,0.0005548472,0.02690166,0.000252056,0.002721289],"genre_scores_gemma":[0.9229305,0.001539669,0.02207069,0.0001629231,0.00005694056,0.0006158725,0.05126961,0.00005367319,0.0013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02516034,"threshold_uncertainty_score":0.02230358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1117427917750679,"score_gpt":0.4865577778266452,"score_spread":0.3748149860515774,"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."}}