{"id":"W2060847891","doi":"10.1002/smll.201101074","title":"Photonic Nose–Sensor Platform for Water and Food Quality Control","year":2011,"lang":"en","type":"article","venue":"Small","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Opalux (Canada); University of Toronto","funders":"","keywords":"Relevance (law); Computer science; Simple (philosophy); Quality (philosophy); Selection (genetic algorithm); Control (management); Fish <Actinopterygii>; Value (mathematics); Data science; Information retrieval; Artificial intelligence; Machine learning; Fishery; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003559079,0.0003589436,0.0003723477,0.0003092283,0.000271753,0.0004540769,0.0007272097,0.0009729576,0.003625032],"category_scores_gemma":[0.0003212435,0.0003253753,0.0002999327,0.0002272838,0.0003256379,0.0006636835,0.0005262001,0.0007149667,0.002001527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000440004,"about_ca_system_score_gemma":0.0006415895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006050743,"about_ca_topic_score_gemma":0.001362323,"domain_scores_codex":[0.9996853,0.00003298888,0.00001000957,0.00005757815,0.0001793495,0.00003474572],"domain_scores_gemma":[0.9998715,0.0000316729,0.00001510977,0.00001138723,0.0000563077,0.00001398334],"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":[0.00008714425,0.00003801365,0.00009827285,0.0000975291,0.00001206896,0.00008361333,0.000009007773,0.0004595373,0.9716631,0.002033997,0.00161898,0.0237989],"study_design_scores_gemma":[0.00003042763,0.0003483254,0.0007773855,0.00002417915,0.00003184619,0.000541637,0.00002609607,0.02312049,0.9413578,0.001570705,0.0321247,0.00004639044],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2690505,0.01790963,0.6484125,0.003079939,0.0028883,0.0005787879,0.001183945,0.005810197,0.0510862],"genre_scores_gemma":[0.5927764,0.005345562,0.3478891,0.002156595,0.0003282149,0.0003259149,0.001024754,0.000191215,0.04996225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003625032,"threshold_uncertainty_score":0.01212692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04809436377187654,"score_gpt":0.2291528372246235,"score_spread":0.181058473452747,"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."}}