{"id":"W1978616547","doi":"10.1016/s0021-9673(99)01318-7","title":"Development of membrane extraction with a sorbent interface–micro gas chromatography system for field analysis","year":2000,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Chemistry; Sorbent; Chromatography; Detection limit; Gas chromatography; Injector; Membrane; Extraction (chemistry); Thermal conductivity detector; Analyte; Tap water; Sample preparation; Analytical Chemistry (journal); Aqueous solution; Adsorption","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003616695,0.0004251777,0.001105476,0.0007733133,0.0001768666,0.0000674949,0.0004876816,0.0002634899,0.001394663],"category_scores_gemma":[0.00001508184,0.0003383035,0.001683116,0.001805663,0.0001614549,0.0002506724,0.00002115414,0.0003479175,0.000002627114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005734877,"about_ca_system_score_gemma":0.0001437536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009832127,"about_ca_topic_score_gemma":0.00001186633,"domain_scores_codex":[0.9968782,0.00002641962,0.001600518,0.000362904,0.0006849693,0.0004469971],"domain_scores_gemma":[0.9975395,0.0002051654,0.001156129,0.0004336966,0.000369471,0.0002960688],"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":[0.003609654,0.001712764,0.01940602,0.005752329,0.02311644,0.000115276,0.001397623,0.0006187772,0.9354496,0.0001463464,0.0007519436,0.007923231],"study_design_scores_gemma":[0.001982957,0.000237479,0.0005690362,0.001003388,0.002763275,0.0003872266,0.001432998,0.0002478168,0.9851646,0.0000340815,0.005739678,0.0004374098],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850942,0.0006358683,0.009338086,0.00005079651,0.00004641044,0.0001193202,0.00002901363,0.00007002165,0.004616224],"genre_scores_gemma":[0.9838564,0.0001019048,0.01578136,0.00001939599,0.0001133387,0.00002378004,0.00001784022,0.00003819006,0.00004775616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04971505,"threshold_uncertainty_score":0.9999069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007518281622457716,"score_gpt":0.241963304140028,"score_spread":0.2344450225175703,"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."}}