{"id":"W2796527059","doi":"10.1051/epjconf/201817605018","title":"Lidar Ice nuclei estimates and how they relate with airborne in-situ measurements","year":2018,"lang":"en","type":"article","venue":"EPJ Web of Conferences","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Aerosol; Ice nucleus; Remote sensing; Backscatter (email); In situ; Effective radius; Particle (ecology); Environmental science; Extinction (optical mineralogy); RADIUS; Atmospheric sciences; Meteorology; Geology; Mineralogy; Physics; Nucleation; Astrophysics; Oceanography; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0001881441,0.0001230326,0.0001671196,0.000002285551,0.00006671986,0.00003545155,0.0001752324,0.00004847522,0.0006178883],"category_scores_gemma":[0.00002417426,0.00008503612,0.00001409097,0.0001007254,0.0005175619,0.0001647579,0.00008491772,0.00006877253,0.00004509353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001745657,"about_ca_system_score_gemma":0.00005093844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004125627,"about_ca_topic_score_gemma":0.00253166,"domain_scores_codex":[0.9991685,0.00002644967,0.0001104637,0.0002282752,0.0002787941,0.0001875302],"domain_scores_gemma":[0.9996505,0.00002826806,0.00009754617,0.0001432281,0.00001853447,0.00006194943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004191153,0.00004352413,0.9795926,0.000007253493,0.00001698699,0.000002678204,0.0006481209,0.00001165595,0.01207413,0.0004420375,0.0001712819,0.006947808],"study_design_scores_gemma":[0.0005187197,0.0004738435,0.9844139,0.0000866081,0.00002081357,0.000003534174,0.0005135061,0.0004532755,0.009761393,0.0008737458,0.002687364,0.0001933051],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9041356,0.00005672128,0.00009112257,0.0004686258,0.00003718007,0.00009309671,8.788214e-7,0.00001618304,0.09510054],"genre_scores_gemma":[0.9970202,0.00003647461,0.002575548,0.00004917629,0.0000168987,0.000004691177,5.653938e-7,0.000007336538,0.0002890377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0948115,"threshold_uncertainty_score":0.6765443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976065065407658,"score_gpt":0.2247198315214682,"score_spread":0.2049591808673916,"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."}}