{"id":"W2892123877","doi":"10.1145/3264907","title":"ID'em","year":2018,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Affordance; Leverage (statistics); Cover (algebra); Scalability; Computer science; Strengths and weaknesses; Context (archaeology); Human–computer interaction; Artificial intelligence; Engineering; Geography; Database; Mechanical engineering","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":[0.0001512748,0.0002215241,0.000251926,0.0002084257,0.0002622388,0.0001421128,0.003297763,0.0001211811,0.00001225612],"category_scores_gemma":[0.0009576441,0.000146818,0.00009444352,0.0004440737,0.0004028713,0.0008701757,0.002892385,0.0003764172,0.00004718438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005820045,"about_ca_system_score_gemma":0.0000199701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001843463,"about_ca_topic_score_gemma":0.000002352893,"domain_scores_codex":[0.998719,0.000008427558,0.0002277809,0.0004809315,0.000230759,0.0003330856],"domain_scores_gemma":[0.9983214,0.0001520269,0.0002849054,0.0006981935,0.0005180382,0.0000254784],"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":[0.0002493801,0.0002871063,0.001754822,0.00006978887,0.0001577075,0.000002092503,0.003432641,0.000001340751,0.8807343,0.0443938,0.01757781,0.05133919],"study_design_scores_gemma":[0.0001747624,0.001225334,0.001858342,0.0002353196,0.00001137646,0.00002693811,0.00653409,0.0001480582,0.9598132,0.02582869,0.003963801,0.0001801338],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830851,0.0002009017,0.0001766919,0.001750387,0.0005070978,0.0004123878,0.000005048718,0.0002231802,0.01363922],"genre_scores_gemma":[0.997917,0.00009820254,0.0011866,0.0002490665,0.00004885856,0.0001011311,1.457537e-7,0.00001282233,0.0003861694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07907884,"threshold_uncertainty_score":0.612812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009772940699900345,"score_gpt":0.2536313500389529,"score_spread":0.2438584093390526,"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."}}