{"id":"W7161818413","doi":"10.82308/34147","title":"Detecting and understanding climate change using hyperspectral radiative measurements","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Radiance; High resolution; Hyperspectral imaging; Interferometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002838666,0.0004279989,0.0002201347,0.0009670384,0.0002700993,0.0009070422,0.0003220012,0.0005227677,0.001127709],"category_scores_gemma":[0.0005120768,0.0001836404,0.0003854197,0.000559681,0.0001910814,0.001047036,0.0004082388,0.0004492334,0.0003677147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002604741,"about_ca_system_score_gemma":0.0003325462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006116934,"about_ca_topic_score_gemma":0.01126017,"domain_scores_codex":[0.9998096,0.0000204657,0.000008865184,0.00005380969,0.00008155056,0.00002584044],"domain_scores_gemma":[0.9998598,0.00003860611,0.00002666667,0.00002070901,0.00004540915,0.000008700009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001780536,0.000444074,0.08597685,0.0003897516,0.0002527821,0.0002577435,0.000472063,0.1199737,0.3316123,0.004745536,0.004420955,0.4512762],"study_design_scores_gemma":[0.00002897882,0.00009276028,0.180155,0.00006556889,0.0001049726,0.0002163209,0.0005630758,0.7229162,0.07663078,0.004290624,0.01484415,0.00009157394],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7378021,0.001470354,0.2344571,0.0007207298,0.0001507081,0.0001773611,0.00302332,0.003517214,0.01868097],"genre_scores_gemma":[0.8765736,0.00109408,0.1175109,0.0001463119,0.00008079509,0.00007524218,0.001799223,0.0001340305,0.002585984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006116934,"threshold_uncertainty_score":0.01216263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128598690740104,"score_gpt":0.2973421319617755,"score_spread":0.1844822628877651,"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."}}