{"id":"W1975857622","doi":"10.1371/journal.pone.0120752","title":"Particle Backtracking Improves Breeding Subpopulation Discrimination and Natal-Source Identification in Mixed Populations","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Oceanic and Atmospheric Administration; Ohio State University; Great Lakes Fishery Commission","keywords":"Biological dispersal; Biology; Population; Backtracking; Ecology; Zoology; Evolutionary biology; Computer science; Demography","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":[0.002421229,0.0005660292,0.0006434225,0.001121957,0.0005048263,0.0007890554,0.0009085754,0.0006432523,0.0007500398],"category_scores_gemma":[0.006927548,0.0004998027,0.0004862853,0.000514477,0.0003541865,0.001226162,0.001086422,0.000459589,0.000321895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004564773,"about_ca_system_score_gemma":0.000713865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006205158,"about_ca_topic_score_gemma":0.01466494,"domain_scores_codex":[0.9993901,0.0001845014,0.00004207226,0.0002524232,0.00008927702,0.00004167038],"domain_scores_gemma":[0.9960319,0.002173168,0.0006498263,0.0006053231,0.0004006107,0.0001390624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009266921,0.0004847696,0.3944778,0.0001955594,0.0005679493,0.0003929821,0.001242974,0.1101437,0.06067358,0.003229097,0.0009108346,0.4267541],"study_design_scores_gemma":[0.00004541177,0.0001970561,0.07260728,0.00003005542,0.0001744372,0.0002477414,0.0001169861,0.9126128,0.0105336,0.002452286,0.0009202731,0.00006213508],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4778855,0.0001941613,0.5195305,0.00006104308,0.00002784784,0.00006015886,0.0001427001,0.0007285786,0.001369408],"genre_scores_gemma":[0.7655494,0.00008591785,0.2331214,0.00006138715,0.00001467902,0.00006205558,0.0002592246,0.00009759278,0.0007484464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006205158,"threshold_uncertainty_score":0.01280487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09541165197364891,"score_gpt":0.2515986841778931,"score_spread":0.1561870322042441,"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."}}