{"id":"W4402694514","doi":"10.1007/s00784-024-05921-x","title":"DeepPlaq: Dental plaque indexing based on deep neural networks","year":2024,"lang":"en","type":"article","venue":"Clinical Oral Investigations","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province","keywords":"Search engine indexing; Dental plaque; Dental research; Dentistry; Computer science; Medicine; Information retrieval","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":[0.0005648763,0.00152392,0.00136349,0.001343969,0.0004196457,0.001245444,0.001798136,0.001382921,0.0075895],"category_scores_gemma":[0.001153395,0.0005719294,0.001116616,0.000854379,0.0002036016,0.0009149502,0.001721834,0.001483547,0.003817755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009108629,"about_ca_system_score_gemma":0.00116974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01257504,"about_ca_topic_score_gemma":0.01788419,"domain_scores_codex":[0.9996587,0.00003023808,0.00002080333,0.0001057733,0.000115981,0.0000685276],"domain_scores_gemma":[0.9997161,0.00007974057,0.00003020901,0.00004284539,0.00009907259,0.00003205591],"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":[0.0008614629,0.0004675463,0.004821289,0.0003261233,0.0002498965,0.0001751285,0.00003873783,0.02490967,0.01444943,0.001072467,0.04546031,0.9071679],"study_design_scores_gemma":[0.0001183456,0.0002427533,0.004881553,0.00008204242,0.0001139137,0.0002784831,0.00004439773,0.963269,0.01695381,0.003979969,0.009971526,0.00006411567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1788938,0.009648441,0.6757745,0.001465993,0.001568163,0.0009280709,0.03270181,0.08662151,0.01239772],"genre_scores_gemma":[0.5055775,0.002318511,0.4221931,0.001496439,0.0005393545,0.0008855935,0.03770051,0.001394113,0.02789494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01257504,"threshold_uncertainty_score":0.02538937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06415116687141606,"score_gpt":0.3663239780015328,"score_spread":0.3021728111301167,"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."}}