{"id":"W4409791202","doi":"10.61091/jcmcc127a-305","title":"Research on pattern recognition and spatial prediction method of lightning activity distribution based on cluster analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Evaluation Methods in Various Fields","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lightning (connector); Cluster (spacecraft); Spatial distribution; Pattern recognition (psychology); Distribution (mathematics); Artificial intelligence; Computer science; Geography; Data mining; Remote sensing; Mathematics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008657545,0.0001613144,0.0004987225,0.0003432091,0.0002757565,0.00008724801,0.0001731504,0.0001947582,0.00002950546],"category_scores_gemma":[0.001199281,0.0001445688,0.0001285859,0.0008199396,0.0001042027,0.0001066992,0.0001738572,0.0006383775,0.000001485852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002143308,"about_ca_system_score_gemma":0.00004855608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001142791,"about_ca_topic_score_gemma":0.0000040455,"domain_scores_codex":[0.9967968,0.001042129,0.0007172978,0.0002467614,0.0009990849,0.0001979379],"domain_scores_gemma":[0.9958327,0.002941086,0.0006808934,0.0002067436,0.0002539495,0.00008456792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003489304,0.007190225,0.03320973,0.001135097,0.001670526,0.00002264913,0.004081474,0.04272539,0.007985522,0.06416152,0.002110028,0.8322185],"study_design_scores_gemma":[0.004283792,0.002228203,0.01687687,0.0006225762,0.0006041333,0.000005401663,0.0001625123,0.6881499,0.01045653,0.2762999,0.00009161496,0.0002185872],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4733486,0.000004802282,0.5234386,0.0001756879,0.002093803,0.0001857289,0.000007225233,0.000008235371,0.0007372097],"genre_scores_gemma":[0.9900708,0.000005835629,0.009663912,0.00003152831,0.000207827,0.00000265472,0.000004788244,0.000009221693,0.000003497939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.832,"threshold_uncertainty_score":0.5895345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05018250660552361,"score_gpt":0.3713874661346469,"score_spread":0.3212049595291233,"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."}}