{"id":"W6925186628","doi":"10.17182/hepdata.44234.v1/t2","title":"Table 2","year":2000,"lang":"en","type":"dataset","venue":"HEPData","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; McGill University","funders":"","keywords":"Meson; Table (database); Luminosity; Photon; Momentum (technical analysis); Sample (material)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002204136,0.0001421476,0.0001692393,0.000341531,0.00009295683,0.0004332037,0.00325708,0.0001840997,0.002169573],"category_scores_gemma":[0.00004290005,0.0001376326,0.00003994722,0.001298709,0.00002944715,0.0003279154,0.000487997,0.0002495406,0.006823702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000184748,"about_ca_system_score_gemma":0.0001386819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004303003,"about_ca_topic_score_gemma":0.00005772665,"domain_scores_codex":[0.9986766,0.00004593688,0.0002078532,0.000511208,0.0003373637,0.0002210593],"domain_scores_gemma":[0.9971569,0.00003218402,0.00008736761,0.002595094,0.00003587106,0.00009262667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[4.519153e-7,0.00004428525,2.560871e-7,0.00001764372,0.00000603531,0.00001145446,0.0000025809,5.717654e-8,3.986449e-7,0.0002785275,0.9958296,0.00380875],"study_design_scores_gemma":[0.00006107258,0.000006643149,0.00001452137,0.000007995332,0.000006005243,0.00001148915,4.069182e-7,0.0001495162,0.000009632513,0.0001187539,0.9994497,0.0001642267],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[3.536734e-7,0.0004131118,0.002857491,0.0002751964,0.0007498526,0.00008517683,0.9950251,0.00007956848,0.0005141427],"genre_scores_gemma":[0.000001286678,0.0008122827,0.001376926,0.000682827,0.0001122394,0.000008088361,0.9950315,0.000003778842,0.001971035],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.004654128,"threshold_uncertainty_score":0.9987426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02648624039813519,"score_gpt":0.2661506052207253,"score_spread":0.2396643648225901,"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."}}