{"id":"W2972899537","doi":"10.1145/3351253","title":"FMT","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Fiducial marker; Computer science; Usability; CLIPS; Object (grammar); Computer vision; Artificial intelligence; Field (mathematics); Human–computer interaction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001634663,0.0001347118,0.0002246913,0.0001715089,0.00009854801,0.0001258119,0.002258391,0.00009378538,0.000007221951],"category_scores_gemma":[0.0005018925,0.00008564653,0.00008430186,0.0004792825,0.00008487399,0.0005763288,0.001865146,0.0002302699,0.00002024872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003285535,"about_ca_system_score_gemma":0.00001256472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001477668,"about_ca_topic_score_gemma":0.00000109025,"domain_scores_codex":[0.9990577,0.000005538798,0.0001944934,0.0003534596,0.000206888,0.0001819101],"domain_scores_gemma":[0.9988564,0.00009902642,0.0002261169,0.0006135522,0.0001908134,0.00001415486],"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":[0.0001394291,0.0004234949,0.06849553,0.0002466197,0.0002880642,0.000001371571,0.001946976,0.0001990345,0.4630235,0.1557967,0.006178948,0.3032604],"study_design_scores_gemma":[0.0002624604,0.0007095265,0.003211895,0.0003174825,0.00002050165,0.00001057179,0.00296768,0.003764948,0.9127719,0.07185297,0.003868758,0.0002413157],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908442,0.0002342119,0.0001549663,0.00195868,0.000175539,0.0003287422,0.000001103762,0.0002951317,0.006007367],"genre_scores_gemma":[0.9976513,0.0001907462,0.001332351,0.00007877299,0.000009072878,0.00005219903,1.265094e-7,0.000006865924,0.0006785679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4497484,"threshold_uncertainty_score":0.419669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006120203254591511,"score_gpt":0.2240112825935602,"score_spread":0.2178910793389687,"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."}}