{"id":"W2898112221","doi":"10.1016/j.imu.2018.10.009","title":"Human Blastocyst's Zona Pellucida segmentation via boosting ensemble of complementary learning","year":2018,"lang":"en","type":"article","venue":"Informatics in Medicine Unlocked","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Hemophilia Society; Pacific Centre for Reproductive Medicine; Simon Fraser University","funders":"","keywords":"Jaccard index; Zona pellucida; Blastocyst; Computer science; Artificial intelligence; Pattern recognition (psychology); Segmentation; Boosting (machine learning); Artificial neural network; Machine learning; Embryo; Biology; Oocyte; Embryogenesis","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":[0.0009357455,0.0006391372,0.0008018551,0.0008511527,0.0003021614,0.0004976621,0.0008584093,0.0006637179,0.0007370682],"category_scores_gemma":[0.001343171,0.0002605869,0.0007817955,0.0004512166,0.0002692911,0.0005130581,0.0007775119,0.0005618862,0.0003977148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004979881,"about_ca_system_score_gemma":0.0007261539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005424391,"about_ca_topic_score_gemma":0.007457212,"domain_scores_codex":[0.9996192,0.0000578673,0.00001647424,0.0001282376,0.0001231971,0.00005496749],"domain_scores_gemma":[0.9995664,0.0001038908,0.00004197146,0.00006082713,0.0001884711,0.00003841097],"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":[0.0003198333,0.0001824376,0.008866115,0.0001138013,0.0002098602,0.0002320873,0.0001305344,0.1244199,0.0704463,0.001392523,0.006039414,0.7876472],"study_design_scores_gemma":[0.000009474673,0.00007089536,0.00284815,0.000008969311,0.00005474358,0.0001069751,0.00001852042,0.9822697,0.01266756,0.0007248229,0.001207977,0.00001218973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1334798,0.001230203,0.8585936,0.0002309636,0.0001114387,0.0001149984,0.00020726,0.002748406,0.003283328],"genre_scores_gemma":[0.7532805,0.0006236635,0.2395915,0.0003135907,0.000083218,0.0001023013,0.0007984476,0.0001236169,0.005083237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005424391,"threshold_uncertainty_score":0.01078564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03762001726324787,"score_gpt":0.3410816099794842,"score_spread":0.3034615927162364,"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."}}