{"id":"W2620800568","doi":"10.4172/1488-5069.1000035","title":"Everything you wanted to know about sperm","year":2002,"lang":"en","type":"article","venue":"Journal of Sexual & Reproductive Medicine","topic":"Reproductive Health and Technologies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Need to know; Sperm; Internet privacy; Biology; Computer science; Computer security; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00203191,0.0003360965,0.001137053,0.0008534017,0.0001426585,0.00001257033,0.0002898243,0.0001465247,0.00052641],"category_scores_gemma":[0.01193368,0.0002226322,0.0000883992,0.001110615,0.0004486651,0.0002860484,0.00009530365,0.001130685,0.0001052483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002981171,"about_ca_system_score_gemma":0.0001083866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004299777,"about_ca_topic_score_gemma":0.000001275084,"domain_scores_codex":[0.9962502,0.00009207401,0.001210371,0.0008162675,0.001077792,0.0005533321],"domain_scores_gemma":[0.9959016,0.0001126105,0.0006733509,0.001106003,0.00170324,0.0005032255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002475932,0.001319208,0.03393293,0.0007151444,0.0006619003,0.001963112,0.0167083,0.0000359696,0.3057281,0.0005307587,0.219779,0.4161496],"study_design_scores_gemma":[0.01131961,0.03951654,0.2183603,0.004115995,0.001328318,0.02381531,0.02875665,0.00007935158,0.05401529,0.001803845,0.6157729,0.001115831],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8630305,0.02046315,0.0003543725,0.1091217,0.002089508,0.000666737,0.00000279261,0.0001262738,0.004145001],"genre_scores_gemma":[0.9764653,0.001410206,0.003365033,0.001858297,0.008261533,0.000009976026,0.0000029514,0.00005779533,0.008568861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4150338,"threshold_uncertainty_score":0.9963892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05121345151393025,"score_gpt":0.3405262583807914,"score_spread":0.2893128068668612,"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."}}