{"id":"W7005316268","doi":"","title":"Radio frequency identification tags used to track construction materials in Toronto subway","year":2011,"lang":"en","type":"article","venue":"NPARC","topic":"Astro and Planetary Science","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Radio-frequency identification; Track (disk drive); Identification (biology); Window (computing); Radio frequency; Key (lock)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003069232,0.0003716183,0.0001903697,0.001977205,0.002647108,0.0009889662,0.0006851609,0.0004298691,0.006205487],"category_scores_gemma":[0.0009042796,0.0003003665,0.000185429,0.002815135,0.0007957041,0.0004978402,0.0006374473,0.0003025878,0.002329687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006695765,"about_ca_system_score_gemma":0.006516489,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7077202,"about_ca_topic_score_gemma":0.8899211,"domain_scores_codex":[0.9994087,0.00003794239,0.00002001289,0.0001145321,0.00032703,0.00009171595],"domain_scores_gemma":[0.9991186,0.00007732799,0.0001357559,0.00007034303,0.0005109272,0.00008701876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001375534,0.0002739739,0.5377365,0.0003957064,0.00006144681,0.00214265,0.01444919,0.009406549,0.1157026,0.002945437,0.02895158,0.2865589],"study_design_scores_gemma":[0.00002948787,0.0005443119,0.8210582,0.0001204052,0.0001306289,0.00043064,0.01955712,0.01752045,0.0646894,0.0003223634,0.07551056,0.00008655972],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9507223,0.0001525273,0.005573059,0.0001880365,0.00005914427,0.0001387875,0.003373695,0.0005154689,0.03927686],"genre_scores_gemma":[0.9380713,0.0001448994,0.006329828,0.00003800668,0.0000066198,0.00003461631,0.001571752,0.00005444372,0.05374856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2922798,"threshold_uncertainty_score":0.5880021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890525555028376,"score_gpt":0.229367535294749,"score_spread":0.2104622797444652,"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."}}