{"id":"W2381085320","doi":"","title":"The High-yielding Multiplying Techniques of Dian-type I CMS Hexi 42 A","year":2012,"lang":"en","type":"article","venue":"Seed","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Type (biology); Mathematics; Environmental science; Biology; Ecology","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":[0.0001825314,0.00006663114,0.0000789781,0.00001779721,0.0001313274,0.00001617228,0.00009562183,0.00001734145,0.00003334085],"category_scores_gemma":[0.000006997037,0.00004667449,0.00002940574,0.0001113569,0.00002737873,0.0001281416,0.00003501355,0.00007018691,0.00003075898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007588752,"about_ca_system_score_gemma":0.00001358248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008362577,"about_ca_topic_score_gemma":0.000001645784,"domain_scores_codex":[0.9995057,0.00001401533,0.0001241087,0.00005653292,0.00007709783,0.0002225198],"domain_scores_gemma":[0.9996939,0.00006286339,0.00005803493,0.000119124,0.00002777999,0.00003825727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002037181,0.00002601419,0.8853557,0.000006273473,0.00003271715,7.09779e-8,0.0007697136,0.000002133446,0.08092438,0.001876137,0.00007210608,0.03091436],"study_design_scores_gemma":[0.0001578739,0.00001851965,0.3278787,0.00002459622,0.000008508117,1.736344e-7,0.0002168597,0.0002480522,0.6654677,0.0002954417,0.005563787,0.0001198134],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968187,0.00007983633,0.0002487621,0.00003778679,0.0002151876,0.00009050568,0.000001797255,0.0000295204,0.002477875],"genre_scores_gemma":[0.9986854,0.000004917623,0.00088408,0.00001168567,0.0002126201,0.0000130492,0.000003048034,0.000007394189,0.000177793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5845433,"threshold_uncertainty_score":0.1903331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620033767210409,"score_gpt":0.2532124187014499,"score_spread":0.2370120810293458,"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."}}