{"id":"W2399454798","doi":"10.1017/s0890060415000219","title":"Three methods for identifying novel affordances","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Open Source Software Innovations","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Affordance; Computer science; Hacker; Product (mathematics); Human–computer interaction; Object (grammar); Natural (archaeology); Artificial intelligence; Computer security; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001759863,0.0002001871,0.0003226164,0.0005139817,0.0001832815,0.0005079828,0.0005276932,0.0000650274,0.000002054479],"category_scores_gemma":[0.0003667992,0.0001975538,0.0001810934,0.000711347,0.00002810349,0.0005448365,0.0001218438,0.00008667067,0.00000265774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004959951,"about_ca_system_score_gemma":0.00003324477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004504998,"about_ca_topic_score_gemma":0.00002800064,"domain_scores_codex":[0.9985292,0.00001485014,0.0004625994,0.000476722,0.0001491711,0.0003674749],"domain_scores_gemma":[0.9985007,0.0007180598,0.0001237649,0.0003631757,0.0001606419,0.0001336574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002114044,0.00005052748,0.00002459736,0.00005293787,0.0006345136,8.318358e-7,0.00102596,0.3976701,0.007510227,0.2097943,0.00003488146,0.3831799],"study_design_scores_gemma":[0.00003139258,0.0000460187,0.00003019326,0.000009334127,0.0001270923,0.000001530295,0.00006343451,0.7079261,0.2478865,0.04253792,0.001141226,0.0001992931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009998253,0.0001198151,0.9976866,0.0001858615,0.0003394785,0.000458643,0.000003426157,0.0002001812,0.000006128188],"genre_scores_gemma":[0.1980342,0.000003452936,0.8016989,0.00002503062,0.00007144501,0.0001346956,0.000005014842,0.00001541188,0.00001182868],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3829806,"threshold_uncertainty_score":0.8056012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1715508123830568,"score_gpt":0.3725477696795614,"score_spread":0.2009969572965046,"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."}}