{"id":"W2322984202","doi":"10.1177/0013916515577635","title":"Trash or Recycle? How Product Distortion Leads to Categorization Error During Disposal","year":2015,"lang":"en","type":"article","venue":"Environment and Behavior","topic":"Environmental Education and Sustainability","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Categorization; Product (mathematics); Distortion (music); Signage; Architectural engineering; Computer science; Business; Engineering; Advertising; Artificial intelligence; Mathematics; Telecommunications","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":[0.0002051346,0.0001952126,0.0001381424,0.00003311214,0.0001713301,0.00004652935,0.0001214435,0.00005653011,0.001317121],"category_scores_gemma":[0.00003907406,0.0001653942,0.00003311994,0.0001080833,0.0001862148,0.0003657293,0.0001685259,0.0001027466,0.0002782249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008068936,"about_ca_system_score_gemma":0.00001092748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006448625,"about_ca_topic_score_gemma":0.0000545136,"domain_scores_codex":[0.9985474,0.00006183028,0.0001851753,0.0005361084,0.0003798048,0.0002896666],"domain_scores_gemma":[0.9992515,0.000007128116,0.00006172251,0.0003070798,0.000002261833,0.0003702609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007698164,0.0005819661,0.9616631,0.00001036949,0.000002148864,0.000009098904,0.002009132,0.0002306258,0.02357208,0.0000044846,0.0007633956,0.01107664],"study_design_scores_gemma":[0.0003732599,0.000143688,0.983938,0.000002672099,0.00004106223,0.00001119337,0.0009417316,0.00001397492,0.006056378,0.00001478706,0.008204885,0.0002584024],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969462,0.00001972539,0.0001728586,0.001748793,0.0001735036,0.0007008194,0.000007746161,0.0000410817,0.0001892769],"genre_scores_gemma":[0.9867225,0.0000166158,0.0006762213,0.00007187254,0.00006823495,0.0002141497,0.00004291485,0.00002291833,0.0121646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02227489,"threshold_uncertainty_score":0.9995958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02119568076531747,"score_gpt":0.2578696938354393,"score_spread":0.2366740130701218,"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."}}