{"id":"W2165986738","doi":"10.1098/rsbl.2009.0433","title":"The conditional economics of sexual conflict","year":2009,"lang":"en","type":"article","venue":"Biology Letters","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Biology; Sexual conflict; Coevolution; Warrant; Sexual selection; Antagonistic Coevolution; Selection (genetic algorithm); Evolutionary biology; Field (mathematics); Positive economics; Economics; Artificial intelligence; Computer science","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":[0.00007760808,0.00004308769,0.00006749944,0.000001828805,0.0001315361,0.000005732405,0.00009935679,0.00002608608,0.00001899067],"category_scores_gemma":[0.00001084276,0.00001211809,0.00002053967,0.00002130755,0.0001208558,0.00001281984,0.00001216687,0.00002985226,0.000009572715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003392265,"about_ca_system_score_gemma":0.000001078427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001715792,"about_ca_topic_score_gemma":0.0001071508,"domain_scores_codex":[0.9997019,0.00002129927,0.00007997778,0.00007730308,0.00001463252,0.0001049518],"domain_scores_gemma":[0.9997435,0.0001869893,0.00004007669,0.000008462366,0.000007900875,0.00001303163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001924383,0.000009100454,0.01201983,2.337127e-7,0.00001315379,4.428733e-7,0.00001020138,6.740772e-7,0.9601575,0.007450281,0.00574237,0.01457696],"study_design_scores_gemma":[0.00005442716,0.0002174167,0.8533871,7.628626e-7,0.000003809742,0.000003607878,0.00004825585,0.000005303404,0.001594748,0.0007944125,0.1438286,0.00006148701],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831606,0.0001307953,9.205538e-7,0.01618647,0.00002915658,0.00002869037,0.00005842705,0.000009379275,0.0003955759],"genre_scores_gemma":[0.9969783,0.00007253059,0.000005685452,0.00273235,0.0001424912,0.000001209715,0.00003316296,7.934896e-8,0.00003418922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9585627,"threshold_uncertainty_score":0.1011683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04428807736028906,"score_gpt":0.2131115525621544,"score_spread":0.1688234752018653,"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."}}