{"id":"W2040991168","doi":"10.1109/conielecomp.2009.63","title":"Tutorial III: Image Processing and Analysis with Matlab","year":2009,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"MATLAB; Computer science; Graphical user interface; Coding (social sciences); Image processing; Source code; Process (computing); Variety (cybernetics); User interface; Computer engineering; Programming language; Engineering drawing; Computer hardware; Software engineering; Human–computer interaction; Artificial intelligence; Image (mathematics); Engineering","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":[0.000374288,0.0001273585,0.0002820287,0.0001555597,0.0007993969,0.0000295216,0.00009400082,0.0001379405,0.0007717218],"category_scores_gemma":[0.00007520046,0.00008876754,0.00003197652,0.0007297943,0.00006843614,0.0002305662,0.00002763841,0.0003685948,0.00009240285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007656893,"about_ca_system_score_gemma":0.000231374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001813232,"about_ca_topic_score_gemma":0.002481746,"domain_scores_codex":[0.9984798,0.0001399661,0.0004297769,0.0002965086,0.0002189383,0.0004349778],"domain_scores_gemma":[0.9990104,0.0001564148,0.0001410074,0.0002217195,0.0003016647,0.0001687483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001266458,0.0002256005,0.7694545,0.0005716963,0.0002736926,0.00008362676,0.03730061,0.00009733117,0.002366432,0.02043104,0.007795398,0.1601336],"study_design_scores_gemma":[0.003679453,0.002950384,0.59052,0.001638253,0.003111311,0.0000258748,0.1338159,0.1768681,0.00588769,0.03401996,0.04392562,0.003557471],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9374353,0.0001655445,0.01389878,0.004827192,0.0003003914,0.000857572,0.000006473395,0.0003490234,0.04215967],"genre_scores_gemma":[0.9901493,0.00001562369,0.005501084,0.001405052,0.0005010752,0.0000237854,0.000008904912,0.00001097868,0.00238421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1789345,"threshold_uncertainty_score":0.8449813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08494534414740082,"score_gpt":0.4766401103724562,"score_spread":0.3916947662250554,"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."}}