{"id":"W4393475419","doi":"10.5281/zenodo.5508261","title":"TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Materials science; Computer graphics (images); Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006038431,0.005388808,0.001982596,0.00379636,0.0008082003,0.00267802,0.003424601,0.002592868,0.006303944],"category_scores_gemma":[0.001885738,0.001226532,0.004228603,0.002433205,0.0008225227,0.001930114,0.002414857,0.00227311,0.0121221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001403177,"about_ca_system_score_gemma":0.001488598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02189947,"about_ca_topic_score_gemma":0.05211942,"domain_scores_codex":[0.9987497,0.0000925808,0.00004957795,0.0005283284,0.0003756859,0.000204189],"domain_scores_gemma":[0.9993473,0.0001316528,0.00005916623,0.0002277382,0.0001483704,0.00008574477],"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":[0.00139551,0.00107856,0.02380406,0.004720381,0.00104696,0.00144465,0.0004007377,0.08609441,0.03740021,0.002291657,0.6359292,0.2043936],"study_design_scores_gemma":[0.0005804409,0.0009257374,0.05649751,0.001236451,0.0006416054,0.00387716,0.001146729,0.4943649,0.07227617,0.007702684,0.3600589,0.0006917344],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09566182,0.007297189,0.05668803,0.000797208,0.0007454279,0.0006041951,0.7595711,0.0685252,0.01010971],"genre_scores_gemma":[0.05297203,0.001110592,0.05616852,0.000209361,0.00007135715,0.0003500094,0.8847069,0.001796046,0.002615307],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02189947,"threshold_uncertainty_score":0.04354399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03710859822394473,"score_gpt":0.21639728732178,"score_spread":0.1792886890978353,"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."}}