{"id":"W4393555292","doi":"10.5281/zenodo.10246857","title":"The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Lightning and Electromagnetic Phenomena","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lightning (connector); World wide; Meteorology; Lightning strike; Upper-atmospheric lightning; Climatology; Environmental science; Geography; Computer science; Geology; Thunderstorm; World Wide Web; Physics","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":[0.001117792,0.0009008222,0.0007343458,0.003996042,0.0003700123,0.001237662,0.001139823,0.000557677,0.01528235],"category_scores_gemma":[0.004197059,0.0003964208,0.0004344863,0.01508103,0.0001336524,0.001462539,0.0009073417,0.0009833159,0.01645101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113138,"about_ca_system_score_gemma":0.003076389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07952192,"about_ca_topic_score_gemma":0.06762602,"domain_scores_codex":[0.9992903,0.00006784777,0.000107217,0.0001046866,0.0003687248,0.00006120947],"domain_scores_gemma":[0.9954972,0.0002680066,0.0006066591,0.0004797772,0.002907724,0.0002406461],"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":[0.00006769222,0.00002661969,0.01086714,0.000453288,0.00004782501,0.00007440019,0.0000852451,0.001129597,0.0003381291,0.00100866,0.9646207,0.02128072],"study_design_scores_gemma":[0.0001051226,0.00003574914,0.09162968,0.0005323952,0.00006274617,0.0001154447,0.0002708401,0.003313504,0.0007388855,0.001275095,0.9018517,0.00006882801],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002149055,0.0001662313,0.0009216811,0.0001420439,0.0001183359,0.00005668505,0.9910282,0.0006507647,0.004766937],"genre_scores_gemma":[0.004321855,0.0003418698,0.002347497,0.00005473199,0.00002778132,0.0001220842,0.9901426,0.000173579,0.002468019],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07952192,"threshold_uncertainty_score":0.1581181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01094062550471384,"score_gpt":0.2270510075159319,"score_spread":0.216110382011218,"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."}}