{"id":"W4234498033","doi":"10.3410/f.1088785.541881","title":"Faculty Opinions recommendation of Gene flow in complex landscapes: testing multiple hypotheses with causal modeling.","year":2007,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gene flow; Flow (mathematics); Computational biology; Computer science; Data science; Psychology; Gene; Biology; Genetics; Mathematics","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001655941,0.0004668559,0.0007235967,0.0003634552,0.0001459029,0.00008088236,0.001274971,0.0007523532,0.00007264899],"category_scores_gemma":[0.01061389,0.000291559,0.0002755059,0.001321393,0.000355898,0.0000271634,0.0004520339,0.0007441079,0.00001238729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005812682,"about_ca_system_score_gemma":0.0006475667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001249148,"about_ca_topic_score_gemma":0.0001237354,"domain_scores_codex":[0.9957728,0.0002456881,0.001517297,0.0005802857,0.001381228,0.0005027475],"domain_scores_gemma":[0.9932206,0.0001399258,0.000793341,0.001068928,0.004459553,0.0003176591],"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":[0.00004959491,0.0003303289,0.00005731629,0.003725738,0.00009632426,4.59839e-7,0.00005393723,0.00001224556,0.00006431033,0.000002531753,0.9921108,0.003496425],"study_design_scores_gemma":[0.001011668,0.0003759332,0.0008927069,0.0034847,0.00005799434,0.00003277764,0.00005826632,0.002216922,0.0001561981,0.000004499893,0.9913751,0.000333205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001898143,0.0005965384,0.0007899275,0.01546645,0.000213457,0.001057649,0.9817832,0.00001286344,0.00006090274],"genre_scores_gemma":[0.0001676675,0.0004147338,0.009477412,0.001560036,0.0003099312,0.00009006286,0.9877941,0.00002714496,0.0001589054],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01390642,"threshold_uncertainty_score":0.9999536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07144119800575309,"score_gpt":0.3582027726948567,"score_spread":0.2867615746891036,"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."}}