{"id":"W6894288568","doi":"10.5683/sp2/7qsvai","title":"Toray 120A","year":2019,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computed tomography; Layer (electronics); Image processing","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002637659,0.0004070501,0.0004940192,0.0002279894,0.00004225504,0.00009192796,0.0009333627,0.0004904825,0.001131171],"category_scores_gemma":[0.0001733742,0.000376028,0.000162078,0.0001780249,0.00006421121,0.00009142228,0.000200213,0.0004194582,0.03406372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001966789,"about_ca_system_score_gemma":0.0001894181,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1087734,"about_ca_topic_score_gemma":0.02939427,"domain_scores_codex":[0.9980642,0.0000975584,0.0002864191,0.0005154779,0.0005981372,0.0004381931],"domain_scores_gemma":[0.9970411,0.0000655623,0.0002684285,0.002383942,0.00009416609,0.0001467726],"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":[0.00002155128,0.00004877877,0.000003725874,0.00008337921,0.00007047304,0.00005073073,0.000006150168,0.000001749513,0.000007919736,0.00001460325,0.9996328,0.00005809682],"study_design_scores_gemma":[0.0002341724,0.00003741091,0.0001967844,0.00006349553,0.0001434362,0.00001103861,0.000004463408,0.000001202883,0.00001392943,0.00002287397,0.9988198,0.0004513524],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[5.190285e-7,0.0001693444,4.648618e-7,0.0000377787,0.0004009879,0.0003404578,0.9941605,0.0001175855,0.004772366],"genre_scores_gemma":[2.721356e-7,0.00006780134,0.00002916839,0.000341458,0.0007243113,0.00003607794,0.9979163,0.0001367423,0.0007478562],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07937911,"threshold_uncertainty_score":0.9998692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03023687488768195,"score_gpt":0.2951635027980929,"score_spread":0.2649266279104109,"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."}}