{"id":"W6903064921","doi":"10.1051/0004-6361/202453277/pdf","title":"Optimizing the hunt for extraterrestrial high-energy neutrino counterparts","year":2025,"lang":"en","type":"article","venue":"Springer Link (Chiba Institute of Technology)","topic":"Astrophysics and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Integrated Electronics Engineering Center, Binghamton University; Institut National de Physique Nucléaire et de Physique des Particules; Stockholms Universitet; Trinity College Dublin; Deutsches Elektronen-Synchrotron; Science and Technology Facilities Council; Queen's University; California Institute of Technology; European Commission; Queen's University Belfast; National Aeronautics and Space Administration; Space Telescope Science Institute; National Science Foundation","keywords":"Neutrino; Blazar; Weighting; COSMIC cancer database; Neutrino detector; Cosmic neutrino background; Statistical power; Cosmic ray; Statistic","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.00523679,0.0005223199,0.0007754091,0.0006900504,0.0005599406,0.001806351,0.001358307,0.001179156,0.002429209],"category_scores_gemma":[0.03159521,0.0005751898,0.0006389627,0.0006645224,0.0009698402,0.001846655,0.001343333,0.0008967103,0.0005751515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000588207,"about_ca_system_score_gemma":0.001179224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004318631,"about_ca_topic_score_gemma":0.003634791,"domain_scores_codex":[0.9988581,0.0005523068,0.00003284279,0.0002388755,0.0001993671,0.0001184914],"domain_scores_gemma":[0.9840514,0.01338679,0.0008526627,0.0006506703,0.0006036892,0.0004547292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002551427,0.000438656,0.2577019,0.0005539365,0.0005792634,0.001015006,0.0005729837,0.4679372,0.02933358,0.0570669,0.006403798,0.1758453],"study_design_scores_gemma":[0.0001938377,0.0002189354,0.01274424,0.00003805822,0.00008726049,0.0002551085,0.0001608171,0.9283183,0.01044966,0.04437646,0.00310467,0.0000526754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6892619,0.001028032,0.3006296,0.001354704,0.0001011176,0.0001024629,0.0006453946,0.001976914,0.004899793],"genre_scores_gemma":[0.8709605,0.0001528446,0.1269332,0.0002696163,0.00003856439,0.00006733771,0.0005410523,0.0001960027,0.0008410431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00523679,"threshold_uncertainty_score":0.02769512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009324281020907405,"score_gpt":0.2273118052694531,"score_spread":0.2179875242485457,"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."}}