{"id":"W6894039998","doi":"10.5281/zenodo.7078559","title":"R code for Demographic consequences of changing environmental periodicity","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Seventh Framework Programme; Horizon 2020 Framework Programme; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Vital rates; Akaike information criterion; Poisson distribution; Marmot; Negative binomial distribution; Poisson regression; Random effects model; Binomial (polynomial); Bayesian probability","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":[0.006960416,0.004076128,0.003462265,0.003187177,0.001111481,0.004011068,0.004601043,0.001629198,0.2817133],"category_scores_gemma":[0.03483877,0.00222855,0.003585657,0.002657279,0.001084082,0.003423553,0.003136572,0.004352446,0.1469422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111542,"about_ca_system_score_gemma":0.004146728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005971014,"about_ca_topic_score_gemma":0.006778936,"domain_scores_codex":[0.9966293,0.0008806354,0.0003742379,0.0009452192,0.0008470426,0.000323437],"domain_scores_gemma":[0.9818185,0.0126261,0.001169373,0.001932351,0.001893099,0.0005605941],"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":[0.0004337703,0.00008850318,0.007164285,0.003343874,0.001332033,0.0003754312,0.0003219957,0.006663973,0.002205061,0.01023588,0.9381022,0.02973298],"study_design_scores_gemma":[0.001671182,0.0002841426,0.0192277,0.001686115,0.001383022,0.001281215,0.0002203155,0.04534572,0.00560353,0.08215011,0.8406339,0.0005130081],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.00527043,0.001002719,0.1634437,0.001273177,0.001045782,0.0008672249,0.5077454,0.3067338,0.01261772],"genre_scores_gemma":[0.04121746,0.001178659,0.36026,0.002794025,0.0005019741,0.0101667,0.2918378,0.2716382,0.02040535],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.2817133,"threshold_uncertainty_score":0.942425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03260552868793427,"score_gpt":0.2419916361841967,"score_spread":0.2093861074962624,"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."}}