{"id":"W1979452287","doi":"10.1051/photon/20199730","title":"Détection et analyse automatique des aérosols atmosphériques par lidar infrarouge","year":2019,"lang":"fr","type":"article","venue":"Photoniques","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Geography; Forestry; Art","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007271705,0.0005187149,0.0005329488,0.00000379322,0.0002651613,0.0002482663,0.0004330744,0.000412452,0.01454254],"category_scores_gemma":[0.0000580404,0.0005092658,0.0002866185,0.0004941713,0.0006500906,0.001223781,0.0003168903,0.0005098082,0.002212474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006581777,"about_ca_system_score_gemma":0.00009158233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01246426,"about_ca_topic_score_gemma":0.001428112,"domain_scores_codex":[0.9970479,0.0003419886,0.0005824637,0.0007825682,0.0004558881,0.0007892108],"domain_scores_gemma":[0.9985658,0.000124518,0.0003046562,0.0006587045,0.00004298669,0.0003033272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008847632,0.0008718323,0.7200971,0.0003365202,0.0002168015,0.000028311,0.01108973,0.001869552,0.2351632,0.003562636,0.004696214,0.0219797],"study_design_scores_gemma":[0.0006905652,0.001195492,0.434401,0.000563739,0.0001362932,0.00006793809,0.001001611,0.01894584,0.3878413,0.008138417,0.145645,0.001372737],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9437127,0.001972673,0.009978167,0.0008970298,0.0005664976,0.0007885891,0.00002861278,0.0004102006,0.04164553],"genre_scores_gemma":[0.9061198,0.003313016,0.07047981,0.00134922,0.0001117644,0.0001061174,0.00001480764,0.00007967716,0.0184258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.285696,"threshold_uncertainty_score":0.9997359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008191871287971527,"score_gpt":0.2551560184821008,"score_spread":0.2469641471941293,"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."}}