{"id":"W1751016217","doi":"10.1038/embor.2008.107","title":"A tale of two strategies","year":2008,"lang":"en","type":"article","venue":"EMBO Reports","topic":"Neuroethics, Human Enhancement, Biomedical Innovations","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biology; Computational biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001665415,0.0000932184,0.0001473336,0.00009934715,0.0001393087,0.00001607929,0.0001204433,0.00003667842,0.0003933336],"category_scores_gemma":[0.0007473785,0.00008779111,0.00004465854,0.0004251739,0.0006501055,0.0002006521,0.00006751613,0.000179052,0.00002633721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001434819,"about_ca_system_score_gemma":0.0001698224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002564801,"about_ca_topic_score_gemma":0.000005426281,"domain_scores_codex":[0.9984961,0.0000417931,0.0004891785,0.0003271095,0.000456121,0.0001896602],"domain_scores_gemma":[0.9990379,0.0001086338,0.0003200441,0.000413868,0.000075795,0.0000438123],"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":[0.00000301351,0.0001575873,0.0006856931,0.0000202171,0.000003257475,0.001833342,0.0003946235,0.00001908261,0.9686145,0.02444092,0.003673492,0.0001543364],"study_design_scores_gemma":[0.0001657601,0.00007901266,0.001984642,0.00002009435,0.000004686994,0.001151208,0.00004627152,0.00002128936,0.9467196,0.04202722,0.007642285,0.0001379008],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9529715,0.00001170465,0.000953669,0.0002971129,0.0005117672,0.0001402923,0.000003500425,0.0000929192,0.04501755],"genre_scores_gemma":[0.9964503,0.00001660514,0.000341833,0.0005852933,0.00006963358,0.00001387372,0.000002111443,0.0000122335,0.002508086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04347885,"threshold_uncertainty_score":0.4306727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08953050361128317,"score_gpt":0.3511359108719647,"score_spread":0.2616054072606815,"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."}}