{"id":"W7084407966","doi":"10.5281/zenodo.13903869","title":"Training datasets with manually labeled TROPOMI data for Machine Learning models [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data]","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Optical properties and cooling technologies in crystalline materials","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"GHGSat (Canada)","funders":"","keywords":"Support vector machine; Satellite; Training set; Convolutional neural network; Methane; Classifier (UML); Channel (broadcasting); Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001297351,0.0003111259,0.0004306166,0.0002083491,0.0005604078,0.0008231185,0.00199113,0.0001033716,0.0003185706],"category_scores_gemma":[0.0002681519,0.0002773243,0.00003179324,0.0002782148,0.0001806832,0.0005377857,0.005388351,0.0005743143,0.00007164827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005004757,"about_ca_system_score_gemma":0.00001500608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002181871,"about_ca_topic_score_gemma":0.000001162982,"domain_scores_codex":[0.9976929,0.0002562286,0.0004617128,0.0008819254,0.0003133705,0.0003938256],"domain_scores_gemma":[0.9979505,0.00007755779,0.0002447616,0.001440601,0.0001946157,0.00009199677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006244585,0.0001736986,0.000001249696,0.001246992,0.0009702934,0.00001941854,0.000198867,0.002370857,0.02319056,0.0001825744,0.8978425,0.07317857],"study_design_scores_gemma":[0.0004731681,0.0002620074,5.559967e-7,0.0002966239,0.0002070019,0.0000172625,0.0003436247,0.07603575,0.000597057,0.0001041839,0.9213685,0.0002942878],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002238319,0.0002981352,0.007354837,0.0001539726,0.000130162,0.0005662462,0.9886123,0.0005539409,0.00009209684],"genre_scores_gemma":[0.01955875,0.0004295612,0.004955421,0.00002547209,0.0001268116,2.767807e-7,0.9741951,0.0006903379,0.00001823652],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0736649,"threshold_uncertainty_score":0.9999679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1305537932114285,"score_gpt":0.3210389285935227,"score_spread":0.1904851353820942,"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."}}