{"id":"W2294860313","doi":"10.1021/acs.est.6b00319","title":"A Model Using Local Weather Data to Determine the Effective Sampling Volume for PCB Congeners Collected on Passive Air Samplers","year":2016,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Recycling and Waste Management Techniques","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Environmental Health Sciences; National Institutes of Health; U.S. Environmental Protection Agency","keywords":"Environmental science; Sampling (signal processing); Volume (thermodynamics); Air monitoring; Meteorology; Environmental engineering; Engineering; Geography; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005987512,0.0002640828,0.0002134256,0.000260004,0.0007101356,0.00003341553,0.001950261,0.0001244171,0.00009440249],"category_scores_gemma":[0.0001711902,0.000165121,0.00005069632,0.0008238788,0.003024633,0.0003058716,0.001880423,0.0001332969,0.0001201791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159215,"about_ca_system_score_gemma":0.00002007087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006039903,"about_ca_topic_score_gemma":0.00005130706,"domain_scores_codex":[0.9974369,0.00003689725,0.0002467394,0.001112388,0.0004586119,0.0007084545],"domain_scores_gemma":[0.998435,0.0001400267,0.0001260275,0.001171122,0.000004541223,0.0001232884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000695269,0.0001341103,0.003793412,0.000001977824,0.00001781663,0.000002899699,0.0001240142,0.03075065,0.6111701,0.000125797,0.001166811,0.3526429],"study_design_scores_gemma":[0.001960659,0.002503146,0.01835805,0.0001636844,0.0001373336,0.00004092257,0.001395482,0.5193033,0.3918751,0.00644603,0.05621003,0.001606275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.644688,0.00000611827,0.3515469,0.001929291,0.00007514887,0.001299717,0.00007772205,0.000155787,0.0002212888],"genre_scores_gemma":[0.975082,0.0000113835,0.02350409,0.0005593747,0.00002045279,0.0002545969,0.000005015617,0.0000294818,0.0005335602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4885526,"threshold_uncertainty_score":0.9996886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04253177648221059,"score_gpt":0.282587348663816,"score_spread":0.2400555721816054,"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."}}