{"id":"W7160155331","doi":"10.5281/zenodo.15526630","title":"Аналіз кількісного складу дентальної біоплівки в залежності від стану твердих тканин зубів","year":2023,"lang":"uk","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Periodontal Regeneration and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Oral cavity; Dental plaque; Streptococcus mutans; Biofilm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005645893,0.0007828388,0.0005953183,0.002786602,0.006072311,0.01754311,0.00127326,0.003223889,0.03660353],"category_scores_gemma":[0.01138309,0.0008401556,0.0009420009,0.002973523,0.01317662,0.01062478,0.005644622,0.005476129,0.0152584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007433912,"about_ca_system_score_gemma":0.01381796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01358848,"about_ca_topic_score_gemma":0.01868553,"domain_scores_codex":[0.9926858,0.002313225,0.0004475027,0.001015561,0.002759355,0.000778453],"domain_scores_gemma":[0.9933239,0.001824459,0.0006043974,0.0007390867,0.002475055,0.001033119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00008523672,0.00007030692,0.003770192,0.0005542026,0.00003893752,0.0004779781,0.01502606,0.0004310523,0.001067004,0.8189167,0.06697007,0.09259222],"study_design_scores_gemma":[0.00001975313,0.00003373375,0.002985756,0.0006630149,0.00003028203,0.000396634,0.008965917,0.0002608848,0.0008191675,0.1423603,0.8434047,0.00005996276],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01993131,0.02426215,0.02604501,0.06855975,0.003520299,0.0002121558,0.001003215,0.0004145431,0.8560515],"genre_scores_gemma":[0.5792778,0.03207112,0.04268079,0.01239475,0.002279414,0.0007594334,0.001345988,0.0009916509,0.328199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03660353,"threshold_uncertainty_score":0.1224511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06277520930889513,"score_gpt":0.2901317195964088,"score_spread":0.2273565102875137,"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."}}