{"id":"W6981572303","doi":"","title":"Episode 0 - 2016 in Review","year":2016,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Politics; Power (physics); Agency (philosophy); Context (archaeology)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01132868,0.001540904,0.003707151,0.009548304,0.002363649,0.01264798,0.00378898,0.00727364,0.2413126],"category_scores_gemma":[0.06675133,0.001056346,0.002695724,0.01412587,0.00118776,0.007359257,0.009480433,0.00505447,0.08179348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01316928,"about_ca_system_score_gemma":0.03633014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0327189,"about_ca_topic_score_gemma":0.07666729,"domain_scores_codex":[0.9881068,0.001442249,0.00217087,0.0007732072,0.005739819,0.001767005],"domain_scores_gemma":[0.9530106,0.005590888,0.009612745,0.002318092,0.02093067,0.008537102],"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":[0.00006502484,0.000007416603,0.00004561789,0.00940183,0.00002369199,0.00002767633,0.00002865602,0.000008355189,0.000031651,0.0005749013,0.9792967,0.01048851],"study_design_scores_gemma":[0.0001067311,0.00001530272,0.0007216498,0.03262523,0.00005322991,0.00006499083,0.00009060948,0.000007642197,0.00005789303,0.0004794039,0.9657628,0.00001453857],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0006866275,0.2315167,0.0006390933,0.1433056,0.1910733,0.00662675,0.2537368,0.001502105,0.1709133],"genre_scores_gemma":[0.009158862,0.2596388,0.002148847,0.2512335,0.06493188,0.01296454,0.1280561,0.00162316,0.2702443],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7586874,"threshold_uncertainty_score":0.8072711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00374250854576384,"score_gpt":0.1902104409233364,"score_spread":0.1864679323775726,"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."}}