{"id":"W1972622093","doi":"10.1117/1.1426082","title":"Comparison of deconvolution techniques using a distribution mixture parameter estimation: Application in single photon emission computed tomography imagery","year":2002,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Deconvolution; Computer science; Imaging science; Artificial intelligence; Computed tomography; Remote sensing; Computer vision; Algorithm; Geology","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":[0.00434146,0.0009790172,0.0009456024,0.00193841,0.0006210127,0.001756564,0.0009415905,0.002172454,0.001185697],"category_scores_gemma":[0.01378898,0.0006030023,0.001118437,0.001312286,0.0005444128,0.002033043,0.001192359,0.001153704,0.0005051151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006995432,"about_ca_system_score_gemma":0.001355306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005189147,"about_ca_topic_score_gemma":0.005780082,"domain_scores_codex":[0.999104,0.0003852292,0.00005783329,0.0001210776,0.0002782858,0.00005343769],"domain_scores_gemma":[0.9929909,0.004884874,0.0003046894,0.0004746632,0.001216828,0.0001280375],"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":[0.00448939,0.0004920203,0.005418604,0.0009153727,0.000787152,0.0001946649,0.0004240861,0.1874917,0.05432289,0.006448586,0.002499116,0.7365164],"study_design_scores_gemma":[0.00009329633,0.0001993935,0.0032843,0.00004877353,0.000179175,0.0004296918,0.00008468859,0.9580036,0.03268477,0.003049061,0.001846596,0.0000966831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07768689,0.002384501,0.9163414,0.0003939032,0.00008528965,0.000124626,0.0001503504,0.001571296,0.001261758],"genre_scores_gemma":[0.212457,0.001895437,0.7834439,0.00009297699,0.00003977819,0.0000804658,0.0002907036,0.0003922903,0.00130735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005189147,"threshold_uncertainty_score":0.02296007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02088375097871304,"score_gpt":0.3295125844139981,"score_spread":0.308628833435285,"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."}}