{"id":"W4408438676","doi":"10.5194/egusphere-egu25-5629","title":"An enhanced NHI algorithm configuration for fire detection and mapping","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Algorithm; Fire detection; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001241687,0.0001881762,0.0002234422,0.0001103776,0.00008414469,0.00008769957,0.00006544276,0.0003230984,0.00001895591],"category_scores_gemma":[0.00001190655,0.0002051586,0.00006236788,0.0000687053,0.000009021256,0.00008022526,0.00002098508,0.000201369,0.00000377678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008080092,"about_ca_system_score_gemma":0.00001680093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007654422,"about_ca_topic_score_gemma":0.00008406154,"domain_scores_codex":[0.9992265,0.00002182748,0.0002743097,0.0002734546,0.0000715589,0.0001323919],"domain_scores_gemma":[0.9995905,0.00003411392,0.0000416196,0.0002112102,0.00007403527,0.00004850077],"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":[0.000008010898,0.000006602687,7.758754e-7,0.0008892062,0.00007158545,2.526834e-7,0.0005222185,0.01146347,0.03772739,0.00003728271,0.0001312765,0.9491419],"study_design_scores_gemma":[0.0002144507,0.00002759245,0.0001100247,0.0001111673,0.00001331796,0.000002022848,0.0002010663,0.9220632,0.07092172,0.0001503789,0.005963337,0.0002217319],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008155289,0.0001486902,0.9836398,0.00002482787,0.002384483,0.0007659575,0.00004997175,0.000784576,0.004046364],"genre_scores_gemma":[0.9922536,0.00009470112,0.005549239,0.00002869645,0.0002796852,0.0003506483,0.00008207574,0.00002276921,0.001338571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9840983,"threshold_uncertainty_score":0.8366123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087590979002736,"score_gpt":0.2323314736638281,"score_spread":0.2214555638738007,"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."}}