{"id":"W4220943881","doi":"10.1101/2022.03.24.485545","title":"sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":183,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Computer science; R package; Random effects model; Mathematics; Computational science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005490288,0.0005129814,0.0005972045,0.0001028647,0.0003672681,0.0002020281,0.0003119088,0.000281509,0.00007133579],"category_scores_gemma":[0.00008953009,0.0004995404,0.00006691291,0.0001852334,0.0001976798,0.0002371871,0.0007585005,0.0004427308,0.000002839181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001169022,"about_ca_system_score_gemma":0.00009668927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00151586,"about_ca_topic_score_gemma":0.0001188516,"domain_scores_codex":[0.9974363,0.0001782072,0.0003729736,0.00111389,0.0004000959,0.0004984687],"domain_scores_gemma":[0.9984236,0.0001548139,0.0003007611,0.0007295532,0.00005669165,0.0003346165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01116284,0.002770152,0.2757249,0.01118851,0.002609383,0.001195632,0.001481277,0.1378499,0.5185906,0.02468087,0.01121258,0.001533389],"study_design_scores_gemma":[0.02723842,0.00320138,0.3053912,0.0008247998,0.001147411,4.340048e-7,0.00005627421,0.5267263,0.1124825,0.0002882624,0.016783,0.005860009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6840081,0.000188164,0.3135635,0.00006052541,0.0003680429,0.001323594,0.0003429744,0.0001344722,0.00001069647],"genre_scores_gemma":[0.8999208,0.0001385915,0.09892198,0.0001668221,0.0001705611,0.0005564755,0.00000543476,0.0001021706,0.00001713917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.406108,"threshold_uncertainty_score":0.9997456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01146466889708274,"score_gpt":0.2179257944969149,"score_spread":0.2064611255998322,"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."}}