{"id":"W2136929769","doi":"10.5194/acp-13-7665-2013","title":"Semi-empirical parameterization of size-dependent atmospheric nanoparticle growth in continental environments","year":2013,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Suomalainen Tiedeakatemia; European Commission; Maj ja Tor Nesslingin Säätiö; Helsingin Yliopisto","keywords":"Cloud condensation nuclei; Aerosol; Particle size; Particle (ecology); Environmental science; Condensation; Atmospheric sciences; Particle-size distribution; Monoterpene; Nanoparticle; Chemistry; Meteorology; Materials science; Nanotechnology; Physics; Geology","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":[0.0005032077,0.0005887187,0.0002676114,0.0003612137,0.0001805148,0.0004423614,0.0008034579,0.0006827459,0.0004711591],"category_scores_gemma":[0.00193532,0.0002722138,0.0005695891,0.0002757861,0.0003721848,0.0008366212,0.0003462561,0.0003917463,0.0001224301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005945428,"about_ca_system_score_gemma":0.0003189621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009023319,"about_ca_topic_score_gemma":0.004687534,"domain_scores_codex":[0.9998622,0.00003523684,0.00001108058,0.00004821447,0.00002641212,0.00001675883],"domain_scores_gemma":[0.9991154,0.0005418392,0.0001050869,0.0001073943,0.0001082016,0.00002220642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001473548,0.00001879512,0.006020044,0.00002113446,0.00002120409,0.00002884955,0.00001422237,0.9875354,0.003846155,0.0003608552,0.0000657175,0.002052837],"study_design_scores_gemma":[0.000002977267,0.00000531137,0.001980248,0.000001268551,0.000002730281,0.000009023165,0.000004839813,0.9966884,0.001004331,0.0001999359,0.00009639481,0.000004564074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8747945,0.0002788308,0.1215251,0.0001063747,0.00001740306,0.00005924757,0.0007797269,0.0003695487,0.002069152],"genre_scores_gemma":[0.9928977,0.00007522768,0.006291818,0.00001429806,0.000004050579,0.00004026333,0.0003980774,0.00003421008,0.0002442822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009023319,"threshold_uncertainty_score":0.01794159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006879434676512051,"score_gpt":0.1943347446436644,"score_spread":0.1874553099671523,"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."}}