{"id":"W2296502461","doi":"10.1109/hicss.2016.83","title":"How to Design Interfaces for Product Recommendation Agents to Influence the Purchase of Environmentally-Friendly Products","year":2016,"lang":"en","type":"article","venue":"","topic":"Color perception and design","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Affect (linguistics); Heuristics; Product (mathematics); Preference; Product design; Interface (matter); Interface design; Human–computer interaction; Marketing; Computer science; Key (lock); Knowledge management; Business; Psychology; Mathematics; Communication","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005224398,0.0001199195,0.0001164013,0.00008115735,0.0000632879,0.00002758606,0.0002463243,0.00003083456,0.00196804],"category_scores_gemma":[0.0003195846,0.00006614462,0.00002790662,0.0001321545,0.000053766,0.0001428926,0.00006515649,0.00003194133,0.0004799689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005526144,"about_ca_system_score_gemma":0.00001445781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000158133,"about_ca_topic_score_gemma":0.000008594003,"domain_scores_codex":[0.9989204,0.0001401448,0.0002024305,0.0004130264,0.0001088294,0.0002151073],"domain_scores_gemma":[0.9992257,0.0001247909,0.00007558858,0.0004329077,0.00006338864,0.00007767044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008991233,0.000252414,0.0002604163,0.00001108607,0.0000399237,6.136401e-7,0.00420079,0.0000338615,0.5633152,0.0005455865,0.1847018,0.2457391],"study_design_scores_gemma":[0.001627136,0.003277802,0.02770894,0.00004326181,0.00003546344,0.00001457716,0.002991676,0.00001410403,0.4391536,0.0003147408,0.5243715,0.0004471669],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5906334,0.000009089819,0.322892,0.08162743,0.0004257729,0.003440568,0.00003173077,0.00005113059,0.0008888909],"genre_scores_gemma":[0.967764,0.00000269052,0.009786554,0.001134774,0.00007027191,0.0005630518,0.000003351842,0.00001559805,0.02065977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3771305,"threshold_uncertainty_score":0.9989443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07615964450648416,"score_gpt":0.337994967166161,"score_spread":0.2618353226596768,"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."}}