{"id":"W7128569344","doi":"","title":"TIPH-1 : Objet fantôme CT fait de poudre de titane dopée","year":2023,"lang":"","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computed tomography; Titanium; Object (grammar); Imaging phantom; Titanium powder; Metal powder; Titanium alloy","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":[0.0004832342,0.0007467242,0.0004253705,0.001077624,0.0003711773,0.001274159,0.0008724004,0.001710066,0.03268442],"category_scores_gemma":[0.000921129,0.0005926461,0.0003549263,0.000600918,0.000388519,0.001209007,0.0006393438,0.00108823,0.01025792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004324138,"about_ca_system_score_gemma":0.0005438668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009744189,"about_ca_topic_score_gemma":0.00105568,"domain_scores_codex":[0.9997761,0.00001466292,0.00001135338,0.00006974297,0.000102385,0.00002581645],"domain_scores_gemma":[0.9997752,0.00007002743,0.00002995206,0.00003736816,0.00005310636,0.00003431628],"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":[0.002704899,0.0002137215,0.005971195,0.001578422,0.0001060043,0.004982339,0.0002778751,0.001226026,0.6962571,0.006547818,0.07183932,0.2082954],"study_design_scores_gemma":[0.0003728377,0.0006933594,0.0188091,0.0003590271,0.0001911434,0.02988135,0.0001631453,0.008156841,0.6399732,0.001841885,0.2993756,0.0001826469],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.2699589,0.01121124,0.3965468,0.005042914,0.001328105,0.001594671,0.04428183,0.03818476,0.2318509],"genre_scores_gemma":[0.4415907,0.004013591,0.3535017,0.00341728,0.0003698091,0.0010058,0.02936861,0.01424939,0.1524831],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.03268442,"threshold_uncertainty_score":0.1093403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0485771977535906,"score_gpt":0.334535005936878,"score_spread":0.2859578081832874,"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."}}