{"id":"W2285920049","doi":"","title":"Embryos without boundaries : breeding & selection","year":2011,"lang":"en","type":"article","venue":"Stockfarm","topic":"Reproductive Health and Technologies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Embryo; Selection (genetic algorithm); Sanctions; Biology; Biotechnology; Political science; Fishery; Law; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001396506,0.00009594991,0.0001687881,0.0001141912,0.0002285422,0.00001690461,0.00005166697,0.00008353595,0.000136818],"category_scores_gemma":[0.000209912,0.0000784487,0.00003739442,0.0002035217,0.0002034414,0.00007647336,0.00002851027,0.0002176004,0.00006615085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006316644,"about_ca_system_score_gemma":0.0001580681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002144187,"about_ca_topic_score_gemma":0.00002596583,"domain_scores_codex":[0.9992266,0.000009231633,0.0001400153,0.0002491224,0.0001307814,0.0002442266],"domain_scores_gemma":[0.9995474,0.000006123674,0.00005289922,0.0002090208,0.000114059,0.00007049401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009812907,0.0002771434,0.8234046,0.0002813645,0.0001244676,0.0000271794,0.002838453,1.589773e-7,0.01929251,0.0456167,0.006555604,0.1006006],"study_design_scores_gemma":[0.002019056,0.003658876,0.6867036,0.0001461274,0.0001667599,0.0006795576,0.001825625,0.00007364229,0.117952,0.02170712,0.1646218,0.0004457578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9261673,0.0003524729,0.001440563,0.0009653161,0.0004709372,0.0005289776,0.000001176577,0.0007340808,0.06933917],"genre_scores_gemma":[0.993064,0.00001531892,0.004532117,0.0001908622,0.0001872602,0.00004196249,0.000001867697,0.00001554002,0.001951113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1580662,"threshold_uncertainty_score":0.3199045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06193505456082803,"score_gpt":0.3084497321645973,"score_spread":0.2465146776037692,"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."}}