{"id":"W2898222722","doi":"10.1038/s41598-018-34431-6","title":"Artificially designed hybrids facilitate efficient generation of high-resolution linkage maps","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science","keywords":"Linkage (software); Hybrid; Computer science; High resolution; Computational biology; Biology; Genetics; Geography; Gene; Remote sensing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001172804,0.0001379186,0.0001810861,0.0001643888,0.0002080386,0.0001167617,0.0001106319,0.00006396007,0.0001464637],"category_scores_gemma":[0.00008496438,0.0001307667,0.00006675455,0.0003638598,0.0002566044,0.000087683,0.0000302316,0.0000690314,0.0001001588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005724263,"about_ca_system_score_gemma":0.00005129339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005121436,"about_ca_topic_score_gemma":0.00005479711,"domain_scores_codex":[0.9981972,0.00003546347,0.0006091867,0.00044428,0.0004320334,0.0002818586],"domain_scores_gemma":[0.9988341,0.0000121903,0.0001161349,0.0006699236,0.0002854477,0.00008223228],"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":[0.000004150615,0.00004050425,0.00004484817,0.00003878836,0.0000154065,0.00004600129,0.0006060161,0.3175158,0.6636354,0.000456378,0.009967497,0.007629252],"study_design_scores_gemma":[0.00003902517,0.00004606586,0.0001672084,0.00002542021,0.00001225801,0.0000297272,0.0000204096,0.1975391,0.7957665,0.001319974,0.004859274,0.0001750515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6741791,0.0001039875,0.3173029,0.0000110343,0.007466093,0.0001926192,0.000006995849,0.0001112255,0.0006260422],"genre_scores_gemma":[0.9954859,0.000003258911,0.003281274,0.000004514515,0.0001829836,0.00001068642,0.00008175494,0.00001665582,0.0009329853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3213068,"threshold_uncertainty_score":0.533251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03769751061061514,"score_gpt":0.2317407353762241,"score_spread":0.194043224765609,"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."}}