{"id":"W4400716374","doi":"10.48550/arxiv.2407.10588","title":"METIS high-contrast imaging: from final design to manufacturing and testing","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Semiconductor Detectors and Materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Metis; Contrast (vision); Computer science; Artificial intelligence; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003372566,0.0008795962,0.0004605199,0.0006784241,0.0004827178,0.00223993,0.002205268,0.001083678,0.003796172],"category_scores_gemma":[0.002931573,0.0006693281,0.0005619528,0.0003756392,0.000544325,0.001238416,0.001035344,0.001400176,0.002681336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002065774,"about_ca_system_score_gemma":0.002257438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001837952,"about_ca_topic_score_gemma":0.001706203,"domain_scores_codex":[0.9975029,0.0001631404,0.00008704357,0.0002140333,0.001830041,0.0002028987],"domain_scores_gemma":[0.9976605,0.0001421869,0.0001822988,0.0002235193,0.001556133,0.0002353177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001005005,0.0003698301,0.008370453,0.001635816,0.0001420397,0.0008389341,0.001234301,0.01855998,0.5221341,0.02835906,0.05595753,0.3613929],"study_design_scores_gemma":[0.0001407365,0.002463846,0.006012572,0.0002822041,0.00009096757,0.001497319,0.0001838286,0.03876587,0.5589597,0.002181803,0.3892553,0.0001659873],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07977419,0.005272123,0.8401192,0.002292672,0.001066797,0.003021675,0.001564348,0.01031171,0.05657725],"genre_scores_gemma":[0.1867084,0.00196932,0.77031,0.0008578623,0.0002411518,0.001156447,0.002857484,0.001829653,0.03406971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003796172,"threshold_uncertainty_score":0.01783603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07535748535589136,"score_gpt":0.1780268513282614,"score_spread":0.1026693659723701,"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."}}