{"id":"W2788102064","doi":"10.3934/biophy.2018.1.36","title":"A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy","year":2018,"lang":"en","type":"article","venue":"AIMS Biophysics","topic":"Digital Holography and Microscopy","field":"Physics and Astronomy","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"California Institute of Technology; Gordon and Betty Moore Foundation","keywords":"Holography; Microscopy; Computer science; Digital holographic microscopy; Speckle noise; Digital holography; Computer vision; Software; Artificial intelligence; Noise (video); Tracking (education); Speckle pattern; Optical microscope; Algorithm; Optics; Physics; Image (mathematics)","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":[0.0009425979,0.001001749,0.0008534436,0.000935815,0.0006559826,0.0008990232,0.001730372,0.001504972,0.002823912],"category_scores_gemma":[0.002966083,0.0005303416,0.0008431672,0.0009428397,0.0005086377,0.0009537943,0.001103052,0.001546702,0.002201597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00066542,"about_ca_system_score_gemma":0.001344497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005025757,"about_ca_topic_score_gemma":0.004514297,"domain_scores_codex":[0.9995593,0.00006956638,0.00003700433,0.000153882,0.0001436091,0.00003657095],"domain_scores_gemma":[0.9992769,0.0003631916,0.00006600181,0.00006627759,0.0001961939,0.00003141034],"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":[0.0001361686,0.0001354077,0.001238862,0.0001468117,0.00009096412,0.0001385551,0.00008087546,0.2652806,0.01257164,0.006374214,0.006560535,0.7072454],"study_design_scores_gemma":[0.00001471657,0.0000329098,0.0002183282,0.00001237563,0.000007977566,0.00005659265,0.000008808626,0.9907761,0.003798429,0.002857942,0.002204646,0.00001118884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003216068,0.0001187266,0.9939656,0.0000783852,0.00003466302,0.0000446067,0.00008735633,0.001882139,0.0005726272],"genre_scores_gemma":[0.03349933,0.0001361941,0.9635036,0.00009111677,0.00002766656,0.0002532419,0.0003525576,0.0001216601,0.002014789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005025757,"threshold_uncertainty_score":0.009992957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02408313643914262,"score_gpt":0.2902535145016127,"score_spread":0.2661703780624701,"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."}}