{"id":"W2377742604","doi":"10.1145/2915926.2915933","title":"A Mobile System for Scene Monitoring and Object Retrieval","year":2016,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laptop; Computer science; Modality (human–computer interaction); Computer vision; Artificial intelligence; Object (grammar); Modalities; Mobile phone; Object detection; Computer graphics (images); Pattern recognition (psychology)","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":[0.00005348703,0.00005614747,0.00007061505,0.00002750437,0.00002707768,0.00001716964,0.00002270757,0.00003664783,0.000003043158],"category_scores_gemma":[0.00001106411,0.00003823759,0.00001637014,0.00004068188,0.000005977045,0.00004072488,0.00000489168,0.00001261815,0.000004884039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004328058,"about_ca_system_score_gemma":0.000003336553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002039101,"about_ca_topic_score_gemma":2.770594e-7,"domain_scores_codex":[0.9996827,0.000003655554,0.00008651515,0.00007502287,0.00004798485,0.0001040987],"domain_scores_gemma":[0.9998177,0.00003930693,0.000007017518,0.00007361678,0.00002615899,0.00003620587],"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":[0.0001088893,0.00002615695,0.006985687,0.001458429,0.0001278708,0.000008087358,0.000358491,0.1192851,0.8095975,0.006389584,0.001137336,0.05451681],"study_design_scores_gemma":[0.001159928,0.0001573489,0.0004987814,0.0002594034,0.00002290018,0.000007655219,0.0002009826,0.3353538,0.6600513,0.00006675883,0.001949207,0.0002718653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.36055,0.0003251572,0.6363989,0.00001129139,0.0006145481,0.0003116417,0.000006934391,0.0004247239,0.00135684],"genre_scores_gemma":[0.9967428,0.00005053627,0.002786976,0.000001253575,0.0001245714,0.000009438414,7.579357e-7,0.00001756107,0.0002660447],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6361929,"threshold_uncertainty_score":0.1559284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105111062915581,"score_gpt":0.2158852558907763,"score_spread":0.2053741495992182,"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."}}