{"id":"W4311180122","doi":"10.1371/journal.pcbi.1010755","title":"Close-kin mark-recapture methods to estimate demographic parameters of mosquitoes","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute of Allergy and Infectious Diseases; Defense Advanced Research Projects Agency; National Institutes of Health","keywords":"Biological dispersal; Biology; Population; Mark and recapture; Kinship; Pupa; Sibling; Larva; Life history theory; Statistics; Demography; Ecology; Life history; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002223097,0.0005101212,0.0005521062,0.000644875,0.0003104731,0.0003386254,0.00146339,0.0004684213,0.002144253],"category_scores_gemma":[0.006477235,0.0003578663,0.0007966085,0.000572819,0.0001909076,0.0007877831,0.0005799693,0.0006721792,0.0006832262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002722138,"about_ca_system_score_gemma":0.0003148888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003154582,"about_ca_topic_score_gemma":0.005176252,"domain_scores_codex":[0.9991003,0.0005060876,0.00005622275,0.0001947432,0.0001074019,0.00003527025],"domain_scores_gemma":[0.9978408,0.001265902,0.0004207786,0.0002607568,0.000176635,0.00003523173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002106246,0.0001567154,0.08017248,0.0006030704,0.0008523364,0.0004717463,0.0006128796,0.5989032,0.008542218,0.02243588,0.00301077,0.2840281],"study_design_scores_gemma":[0.00002102207,0.0001203831,0.01757403,0.00003763993,0.0000813453,0.0004855977,0.00007463355,0.9690666,0.002668875,0.00671964,0.003076505,0.00007366562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05913873,0.0006701532,0.9375834,0.00004976599,0.00002167412,0.0001232691,0.0006074045,0.0004662207,0.001339524],"genre_scores_gemma":[0.6208321,0.0005267604,0.3738634,0.00008404464,0.00004327527,0.0003719923,0.001666216,0.0001030673,0.002509099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003154582,"threshold_uncertainty_score":0.01175702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02085851487252037,"score_gpt":0.3545363799180078,"score_spread":0.3336778650454875,"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."}}