{"id":"W13596017","doi":"","title":"Could avian radar have prevented US Airways Flight 1549’s bird strike?","year":2009,"lang":"it","type":"article","venue":"Archivio Italiano di Anatomia e Istologia Patologica","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Aeronautics; Radar; Aviation safety; Wildlife; Aviation; Situation awareness; Engineering; Telecommunications; Ecology; Biology","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":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.001093675,0.001819163,0.002194904,0.0007214275,0.001609445,0.0008851322,0.005494809,0.001346342,0.0005486294],"category_scores_gemma":[0.0005978929,0.001604111,0.00100309,0.001565191,0.001307741,0.0009244892,0.001289149,0.00261926,0.001714798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003204483,"about_ca_system_score_gemma":0.0004495023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003311897,"about_ca_topic_score_gemma":0.0001451086,"domain_scores_codex":[0.9878289,0.001650952,0.002320463,0.003562468,0.001431608,0.003205645],"domain_scores_gemma":[0.9922531,0.001042082,0.001307174,0.003851069,0.0003261268,0.001220396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001202687,0.005687202,0.319032,0.0002563219,0.0007438925,0.01375945,0.002926528,0.0008239656,0.001567557,0.154846,0.2906235,0.2085309],"study_design_scores_gemma":[0.007822101,0.006615362,0.5232242,0.001697717,0.0007768544,0.003063985,0.0006604777,0.07089816,0.004106316,0.04164303,0.3306666,0.008825236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7170732,0.01328538,0.1594149,0.02633403,0.01181108,0.005298889,0.002002214,0.009089226,0.05569111],"genre_scores_gemma":[0.9778981,0.001322436,0.01470247,0.003226611,0.0003828921,0.00004817894,0.0003276939,0.0001162293,0.001975445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2608249,"threshold_uncertainty_score":0.9999501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01892598282950709,"score_gpt":0.2545413566887064,"score_spread":0.2356153738591993,"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."}}