{"id":"W2795979476","doi":"10.1145/3173574.3173948","title":"HCI meets Material Science","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"Glycemic Index Laboratories","funders":"Engineering and Physical Sciences Research Council; Leverhulme Trust","keywords":"Affordance; Computer science; Human–computer interaction; Context (archaeology)","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":[],"consensus_categories":[],"category_scores_codex":[0.00008056701,0.00006531811,0.00006385703,0.00004199563,0.00008135816,0.00005871371,0.0001186154,0.00001925643,0.0007014867],"category_scores_gemma":[0.00002346974,0.00005594289,0.000007929163,0.0001178993,0.0001770685,0.0001775736,0.00002512136,0.00001429793,0.0002539661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001895043,"about_ca_system_score_gemma":0.000006431494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001096759,"about_ca_topic_score_gemma":0.000004788529,"domain_scores_codex":[0.9995158,0.000002974785,0.0000855164,0.0001012779,0.00008656439,0.0002078546],"domain_scores_gemma":[0.9997691,0.000005240839,0.000006193798,0.000132385,0.00003287037,0.00005422378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000230474,0.000001435033,0.000005567215,0.000004094912,0.000001348737,9.880969e-7,0.00002647879,0.0009016094,0.994809,0.002876719,0.0004166521,0.0009537719],"study_design_scores_gemma":[0.00005141839,0.00001521854,0.0003153831,0.000004583475,0.000001192349,0.000005541288,0.000005967064,0.001407864,0.9807569,0.0003744837,0.0169669,0.00009453093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8666447,0.000002581025,0.001727595,0.00001227836,0.001878683,0.00002060492,0.000002402141,0.000606716,0.1291045],"genre_scores_gemma":[0.9919451,0.000002572218,0.007100842,0.00003335251,0.0004398796,0.000001868924,9.63259e-7,0.0000134049,0.0004619528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1286425,"threshold_uncertainty_score":0.7680787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01116533314271575,"score_gpt":0.2314487099793368,"score_spread":0.2202833768366211,"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."}}