{"id":"W3173271410","doi":"10.1096/fasebj.2019.33.1_supplement.444.5","title":"Photogrammetry or 3D Surface Scanning – Which tool works best for anatomical specimens?","year":2019,"lang":"en","type":"article","venue":"The FASEB Journal","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Island Health; University of British Columbia","funders":"","keywords":"Photogrammetry; Computer science; Workflow; Laser scanning; 3d scanning; Process (computing); Scanner; Artificial intelligence; Mobile device; Computer vision; Stereoscopy; Computer graphics (images); Optics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004577439,0.0001413899,0.0002225402,0.00006300105,0.0001233702,0.00006017535,0.0003337337,0.0001943741,0.0006405156],"category_scores_gemma":[0.0001143352,0.00008934463,0.00008328613,0.0002847867,0.00005814819,0.00009385512,0.00004084486,0.0008762671,0.0001360749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007789589,"about_ca_system_score_gemma":0.00005070383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004788977,"about_ca_topic_score_gemma":0.00001120428,"domain_scores_codex":[0.9990053,0.00002286389,0.0002400239,0.0001150737,0.0001767501,0.0004399519],"domain_scores_gemma":[0.9993717,0.0002037807,0.00004639132,0.0002098283,0.00005109486,0.0001172064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001153266,0.0003028815,0.006817389,0.0002925015,0.001351496,0.0002833199,0.001057219,0.03488133,0.1350643,0.003532822,0.03798461,0.7772788],"study_design_scores_gemma":[0.007452207,0.001142541,0.0005640816,0.001280034,0.0003167375,0.002603838,0.004582405,0.550935,0.1500315,0.002889875,0.2767763,0.001425464],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973322,0.001232025,0.02248204,0.0003198215,0.0008680883,0.0002292527,0.000005057507,0.0001434079,0.001398348],"genre_scores_gemma":[0.9951476,0.0005332514,0.003412148,0.0001334761,0.0002770758,0.000004349516,0.00000227496,0.00003197516,0.0004578193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7758534,"threshold_uncertainty_score":0.7013197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01313714436968842,"score_gpt":0.2442951919316788,"score_spread":0.2311580475619904,"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."}}