{"id":"W4251625579","doi":"10.1086/688286","title":"Commentary","year":2016,"lang":"en","type":"article","venue":"Journal of the Association for Consumer Research","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Generosity; Download; Library science; Consumer research; Altmetrics; Political science; Computer science; Advertising; Law; Business; World Wide Web","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01011126,0.001134312,0.001706195,0.001925173,0.008073929,0.008878532,0.005381264,0.07999206,0.04537072],"category_scores_gemma":[0.1092633,0.001074337,0.002564236,0.001685101,0.006020191,0.004669976,0.004438832,0.05606007,0.02582925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01069512,"about_ca_system_score_gemma":0.02011518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02693591,"about_ca_topic_score_gemma":0.03501268,"domain_scores_codex":[0.9850628,0.002953202,0.0009407995,0.002573245,0.0061714,0.002298511],"domain_scores_gemma":[0.9423411,0.02882047,0.002769744,0.002002097,0.01738952,0.006677039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001420917,0.000004049185,0.00004643018,0.00004317833,0.000006796661,0.00008956764,0.00007398494,0.000005966273,0.00001959981,0.001664894,0.9967554,0.001275838],"study_design_scores_gemma":[0.00003528603,0.000009974433,0.000236676,0.0003943599,0.00002029662,0.0001284051,0.0003050676,0.00002065296,0.00007466712,0.002400839,0.9963535,0.00002024415],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001309849,0.001455569,0.00003956621,0.9196431,0.07096767,0.00001454878,0.0001093224,0.00002047337,0.007618773],"genre_scores_gemma":[0.001148543,0.0005531836,0.00004773544,0.9626415,0.02259179,0.00003680563,0.00003733475,0.00002428638,0.01291888],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9546293,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09792781574425348,"score_gpt":0.413359747568262,"score_spread":0.3154319318240085,"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."}}