{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000317102,0.0001127993,0.0001542367,0.00002166144,0.00002343056,0.000006206066,0.00009203605,0.0001101087,0.00001687386],"category_scores_gemma":[0.00004442955,0.00008474719,0.00003775251,0.00001893388,0.00004759696,0.00004660649,0.00002070776,0.00009739734,0.00001016677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002546355,"about_ca_system_score_gemma":6.14813e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004300299,"about_ca_topic_score_gemma":0.0000175954,"domain_scores_codex":[0.9994195,0.000001828398,0.0001385612,0.0001308756,0.0000342152,0.0002750726],"domain_scores_gemma":[0.999705,0.00005933137,0.00001211633,0.0001690853,0.0000173741,0.00003706991],"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":[0.00006108801,0.0000212094,0.0003535164,0.0001640431,0.00008338788,0.000001749184,0.0003621954,0.0001067293,0.9909009,0.002236121,0.00004694219,0.00566211],"study_design_scores_gemma":[0.0007214037,0.00007342036,0.0002155623,0.00000538125,0.00001092944,0.000002474442,0.00008178377,0.0005723989,0.9852414,0.009663503,0.003248074,0.0001636299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893461,0.0001216475,0.008169588,0.00002479323,0.00007606149,0.0002411752,0.00002779461,0.0006712191,0.001321554],"genre_scores_gemma":[0.9930728,0.00001633892,0.006733614,0.00003359263,0.00001879354,0.00004833824,0.000004221963,0.00002451318,0.0000478059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007427382,"threshold_uncertainty_score":0.345589,"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."}}