{"id":"W2229649997","doi":"10.1371/journal.pbio.1002350","title":"Forecasting Ecological Genomics: High-Tech Animal Instrumentation Meets High-Throughput Sequencing","year":2016,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Vetenskapsrådet; Svenska Forskningsrådet Formas","keywords":"Biology; Genomics; Population genomics; Scope (computer science); Ecological genetics; Instrumentation (computer programming); Population genetics; Ecology; Population; Data science; Computational biology; Evolutionary biology; Genome; Genetics; Computer science; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01151448,0.0009122485,0.001080697,0.001762159,0.0007650707,0.003679517,0.001266215,0.002338884,0.002247789],"category_scores_gemma":[0.02306724,0.0008421619,0.0006941391,0.003257098,0.001692518,0.006549437,0.002291205,0.002904885,0.001042178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001987339,"about_ca_system_score_gemma":0.001700388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004323579,"about_ca_topic_score_gemma":0.00696284,"domain_scores_codex":[0.9973509,0.001289592,0.00009346218,0.0004829441,0.0006264055,0.0001566514],"domain_scores_gemma":[0.9840591,0.01029479,0.001331521,0.001586696,0.001949685,0.0007781967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000636797,0.0003294336,0.2704683,0.001206096,0.0005951803,0.0003868123,0.001077329,0.1480639,0.06695852,0.0725114,0.03406129,0.403705],"study_design_scores_gemma":[0.00007179945,0.0003519373,0.1568683,0.0003472104,0.0003099993,0.000302042,0.00145171,0.3313937,0.01281632,0.411298,0.08454041,0.0002486354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1284179,0.008771948,0.8210275,0.02003759,0.001081023,0.0002178453,0.003356501,0.002486255,0.01460348],"genre_scores_gemma":[0.5245434,0.005438919,0.460208,0.003015618,0.001035978,0.0002745712,0.00327988,0.0005531362,0.00165041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01151448,"threshold_uncertainty_score":0.06089514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06632171107563928,"score_gpt":0.2511384954522672,"score_spread":0.1848167843766279,"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."}}