{"id":"W4405192110","doi":"10.1007/978-3-031-69146-1_37","title":"Abortion Detection and Monitoring for Empowering Equality Through the Integration of Biometric and Artificial Intelligence","year":2024,"lang":"en","type":"book-chapter","venue":"Springer proceedings in physics","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Biometrics; Abortion; Artificial intelligence; Computer science; Biology; Pregnancy","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":[0.001357833,0.0003461929,0.0004417188,0.001212861,0.0003756706,0.00160966,0.0008940758,0.0008648575,0.005414526],"category_scores_gemma":[0.003180921,0.0001888995,0.0003872961,0.000924563,0.0005282631,0.001457241,0.001163964,0.0008905627,0.001710567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004109917,"about_ca_system_score_gemma":0.0007385742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001695591,"about_ca_topic_score_gemma":0.002678377,"domain_scores_codex":[0.9991795,0.0002946163,0.0000329715,0.0001483013,0.0002679819,0.00007657796],"domain_scores_gemma":[0.9990313,0.000561471,0.0001111111,0.00009236568,0.0001634826,0.00004029143],"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":[0.0001466604,0.0001654886,0.01927695,0.0002621545,0.00004654477,0.0002083817,0.000352218,0.003030561,0.01426631,0.02743183,0.01899213,0.9158208],"study_design_scores_gemma":[0.00006353317,0.0006406643,0.1097733,0.001047717,0.000354145,0.003680687,0.002670965,0.3206651,0.09817649,0.1991549,0.2634395,0.0003330205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.09654696,0.02122834,0.7385152,0.007429081,0.001164799,0.0003762103,0.00348404,0.002498532,0.1287569],"genre_scores_gemma":[0.6663156,0.006499319,0.2952875,0.001252986,0.0004669054,0.0001998894,0.001757212,0.0001668522,0.02805387],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.005414526,"threshold_uncertainty_score":0.01811337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1292712747025506,"score_gpt":0.3729883308621359,"score_spread":0.2437170561595853,"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."}}