{"id":"W2987195591","doi":"10.7554/elife.42014.049","title":"Author response: An integrative genomic analysis of the Longshanks selection experiment for longer limbs in mice","year":2019,"lang":"en","type":"peer-review","venue":"","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Selection (genetic algorithm); Genome; Biology; Evolutionary biology; Adaptation (eye); Computational biology; Negative selection; Genetics; Computer science; Gene; Machine learning","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":[0.004716383,0.0008128588,0.000951202,0.0009562834,0.001102069,0.0009233209,0.001236621,0.002172776,0.1377897],"category_scores_gemma":[0.01123978,0.00059794,0.0009485581,0.0009571175,0.0007990226,0.0009947736,0.001556377,0.002741337,0.02986812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001229314,"about_ca_system_score_gemma":0.001644705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002408842,"about_ca_topic_score_gemma":0.007640232,"domain_scores_codex":[0.9974977,0.0004456303,0.0001289316,0.0004525066,0.001230704,0.0002444577],"domain_scores_gemma":[0.9949827,0.002450269,0.000389578,0.0006491493,0.001081671,0.0004466436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001271661,0.0001187706,0.00290019,0.0004301196,0.00008912314,0.000597445,0.0001905687,0.0007405499,0.01970809,0.003888157,0.9419829,0.02808246],"study_design_scores_gemma":[0.001541743,0.0006025244,0.01981061,0.0004587271,0.0001737685,0.0008430735,0.0009797795,0.003212866,0.03445844,0.01195998,0.9256788,0.0002796542],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.05950978,0.002183716,0.1375429,0.1677065,0.07678486,0.002807313,0.3815402,0.04610379,0.1258209],"genre_scores_gemma":[0.1817441,0.003259898,0.2074524,0.09113247,0.006908905,0.005656046,0.1577,0.02101373,0.3251325],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1377897,"threshold_uncertainty_score":0.4609524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01986689515568829,"score_gpt":0.34794040509822,"score_spread":0.3280735099425317,"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."}}