{"id":"W2972177517","doi":"10.1016/j.matchemphys.2019.122119","title":"Scalable sensing of hydrocarbon pollutants using soluble chemiresistive polymer composites","year":2019,"lang":"en","type":"article","venue":"Materials Chemistry and Physics","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Toronto","keywords":"Hydrocarbon; Scalability; Leak detection; Polymer; Pollutant; Fossil fuel; Materials science; Computer science; Environmental science; Process engineering; Chemical engineering; Leak; Composite material; Chemistry; Organic chemistry; Engineering; Environmental engineering","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":[0.00006021159,0.0002163818,0.0004228635,0.000003758224,0.00004677198,0.00002761332,0.00009119351,0.0001458079,0.0003612053],"category_scores_gemma":[0.00001903807,0.0002190003,0.00006034828,0.00009662964,0.00009302873,0.0000728009,0.00008437376,0.0001075161,0.00000863951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002780763,"about_ca_system_score_gemma":0.0000172716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003891714,"about_ca_topic_score_gemma":3.88807e-8,"domain_scores_codex":[0.9990085,0.00000928583,0.0002853289,0.0002732438,0.0001498955,0.000273733],"domain_scores_gemma":[0.9994601,0.00006537513,0.0001208403,0.0002226135,0.00003723144,0.0000938561],"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":[0.00006392189,0.00002846394,0.00009536033,0.0006545699,0.0000544027,0.000002615742,0.00004908446,0.0002191609,0.9986772,0.0000697311,0.00000404241,0.00008142443],"study_design_scores_gemma":[0.0002967534,0.000005591506,0.00001272079,0.0001608794,0.00005390281,0.00001020718,0.00004201915,0.02495914,0.9740206,0.0001837692,0.00001356131,0.0002408293],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995782,0.00008168011,0.0002553342,0.00001144284,0.00005521651,0.0000497305,0.00005744513,0.00004131187,0.003665856],"genre_scores_gemma":[0.9980784,0.000005143787,0.0004205186,0.00001794477,0.0001862515,4.171965e-7,0.00002449414,0.00002691978,0.001239894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02473998,"threshold_uncertainty_score":0.8930572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009780919917936103,"score_gpt":0.2119734037150668,"score_spread":0.2021924837971307,"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."}}