{"id":"W2068752744","doi":"10.1016/j.actbio.2009.08.007","title":"Geometric analysis of porous bone substitutes using micro-computed tomography and fuzzy distance transform","year":2009,"lang":"en","type":"article","venue":"Acta Biomaterialia","topic":"Bone Tissue Engineering Materials","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Canada Research Chairs","keywords":"Porosity; Materials science; Reproducibility; Permeability (electromagnetism); Biomedical engineering; Image processing; Tomography; Emulsion; Composite material; Mineralogy; Image (mathematics); Mathematics; Computer science; Chemical engineering; Artificial intelligence; Optics; Statistics; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001416562,0.0002631476,0.0006231341,0.001082759,0.00003805836,0.00009065557,0.000142775,0.0001204863,0.00003828677],"category_scores_gemma":[0.000007380802,0.0002732923,0.00009875651,0.00219484,0.00004976021,0.0001933609,0.00001356638,0.00003294557,0.000001586792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003823054,"about_ca_system_score_gemma":0.000005281644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007483221,"about_ca_topic_score_gemma":0.000006316889,"domain_scores_codex":[0.9988217,0.00001503571,0.0004772359,0.0002262351,0.0001415839,0.0003182599],"domain_scores_gemma":[0.9995152,0.00001625373,0.00007349944,0.0002796131,0.00003206754,0.00008333662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001973012,0.00002427145,0.0001997848,0.0001238037,0.0003995115,0.0000117939,0.0001385177,0.001421471,0.9968149,0.00005501972,0.00005667457,0.0007345123],"study_design_scores_gemma":[0.00047411,0.00006103257,0.07020176,0.00006505681,0.0007665379,0.00002266243,0.000009770708,0.004123429,0.9231899,0.00009080882,0.0004946074,0.0005002935],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935678,0.001152543,0.003747369,0.00002129354,0.0007851332,0.0001487801,0.0001882206,0.0003469778,0.00004185736],"genre_scores_gemma":[0.9950449,0.00006444955,0.004709556,0.000005796734,0.00006086668,0.0000027801,0.00007486204,0.00003001883,0.000006760861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07362498,"threshold_uncertainty_score":0.9999719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008699684398939457,"score_gpt":0.2111786842262821,"score_spread":0.2024789998273426,"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."}}