{"id":"W3189592896","doi":"10.20944/preprints202108.0181.v1","title":"Remote Sensing of Aerosols at Night with the CoSQM Sky Brightness Data","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep de Sherbrooke; Bishop's University; Université de Sherbrooke","funders":"Kansainvälisen Liikkuvuuden ja Yhteistyön Keskus","keywords":"Aerosol; Environmental science; Remote sensing; Sky brightness; Photometer; Brightness; Angstrom exponent; Sun photometer; Sky; Zenith; Meteorology; Daytime; Atmospheric sciences; Physics; Geology; Optics","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":[0.0003421902,0.0002967827,0.0002230447,0.0005665715,0.0001600793,0.0002528864,0.000318005,0.0002316411,0.0006648936],"category_scores_gemma":[0.0004023024,0.0001031116,0.000237699,0.0005371125,0.0001121288,0.0002254548,0.000359926,0.0001832855,0.0002327951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003155509,"about_ca_system_score_gemma":0.0002621328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01075269,"about_ca_topic_score_gemma":0.02027597,"domain_scores_codex":[0.9997082,0.00003551966,0.00001142041,0.00009906897,0.0001120518,0.00003374154],"domain_scores_gemma":[0.9998469,0.0000193153,0.00003153709,0.00003186979,0.00005472092,0.00001559476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006459353,0.0003822523,0.5172365,0.0002557243,0.0002224554,0.0002557959,0.0003048595,0.02423964,0.3250787,0.0007374239,0.003627435,0.1270134],"study_design_scores_gemma":[0.00003594918,0.0001085481,0.8974411,0.000012371,0.00003052312,0.00009335273,0.00007595259,0.06745328,0.03100468,0.0001164028,0.003605075,0.00002269151],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983732,0.0001740516,0.008718516,0.00003162323,0.0000233254,0.00004070622,0.003410806,0.0003143978,0.003554558],"genre_scores_gemma":[0.9817742,0.00004906986,0.01450286,0.00002600053,0.00001298829,0.00003135976,0.002927177,0.00003029675,0.0006460426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01075269,"threshold_uncertainty_score":0.02138019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1409458936792603,"score_gpt":0.3375176387659194,"score_spread":0.1965717450866591,"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."}}