{"id":"W2373114253","doi":"10.1016/j.jmbbm.2016.04.041","title":"Compensation strategy to reduce geometry and mechanics mismatches in porous biomaterials built with Selective Laser Melting","year":2016,"lang":"en","type":"article","venue":"Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":157,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; China Scholarship Council","keywords":"Selective laser melting; Materials science; Porosity; Compensation (psychology); Composite material; Biomaterial; Mechanical engineering; Nanotechnology; Engineering; Microstructure","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001101022,0.000231234,0.0001908181,0.0002000989,0.0001895229,0.0002190687,0.0004767597,0.0003159154,0.0006641387],"category_scores_gemma":[0.0001967881,0.0001692056,0.0002138998,0.0001856882,0.0002157741,0.0003011129,0.0003433944,0.0002073119,0.0001488808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002133857,"about_ca_system_score_gemma":0.0002285241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000246893,"about_ca_topic_score_gemma":0.0006513098,"domain_scores_codex":[0.9998899,0.000005718186,0.000006516636,0.00002488324,0.00004757238,0.00002545744],"domain_scores_gemma":[0.9998668,0.00002187748,0.0000527804,0.0000193158,0.00002808516,0.00001102285],"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":[0.00004626621,0.00001451715,0.0002024727,0.00002534239,0.000005006811,0.00003097584,0.00001269332,0.001506279,0.994255,0.0004285715,0.00006550324,0.003407493],"study_design_scores_gemma":[0.00001051852,0.00008212785,0.0005960347,0.000002194303,0.00001230298,0.0000749051,0.00001578319,0.0297635,0.9686538,0.0001061484,0.0006738174,0.00000876362],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9183588,0.0004015225,0.07872772,0.0001002681,0.00005847963,0.00002204168,0.00004265058,0.0003518807,0.001936667],"genre_scores_gemma":[0.9911328,0.00005199871,0.008183332,0.00002130614,0.000005932283,0.000007978125,0.00001884215,0.000014424,0.0005634608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006641387,"threshold_uncertainty_score":0.002221763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896754800684678,"score_gpt":0.2569828283440718,"score_spread":0.238015280337225,"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."}}