{"id":"W2949483509","doi":"10.1145/3300961","title":"SMAC","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Rheumatology Association; University of Toronto","funders":"","keywords":"Proxemics; Human–computer interaction; Workflow; Desk; Computer science; Work (physics); Gaze; Engineering; Artificial intelligence","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":[0.0007134773,0.0001175941,0.0001687374,0.0002853562,0.0001144492,0.0003286838,0.002290601,0.00004105199,0.0006080546],"category_scores_gemma":[0.0002314837,0.0000725487,0.000174688,0.000338895,0.00002791068,0.00121686,0.0007483375,0.0001769243,0.001119897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004760056,"about_ca_system_score_gemma":0.00000496293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007344384,"about_ca_topic_score_gemma":8.765053e-7,"domain_scores_codex":[0.9981992,0.000009170237,0.0004828311,0.0002534865,0.0009208837,0.0001344561],"domain_scores_gemma":[0.9984582,0.0001375109,0.000521508,0.0004866894,0.0003651506,0.00003095626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003132435,0.0004476373,0.1841377,0.00009983155,0.0001055342,4.896899e-7,0.005738641,0.0004320735,0.05148384,0.05839667,0.5764833,0.122361],"study_design_scores_gemma":[0.001775068,0.000889889,0.6491908,0.0004305226,0.00006887402,0.00001971219,0.003315072,0.01757533,0.06901443,0.05165065,0.205341,0.0007286274],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740288,0.000001515461,0.00002865219,0.001392504,0.001302701,0.0002270508,0.00000139591,0.00003790234,0.02297948],"genre_scores_gemma":[0.9922615,6.497586e-7,0.000570669,0.0006563817,0.0001543329,0.000006456364,9.867488e-7,0.000007084976,0.00634189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4650531,"threshold_uncertainty_score":0.9996579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3097970958595432,"score_gpt":0.4576377586033961,"score_spread":0.1478406627438529,"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."}}