{"id":"W4210864950","doi":"10.1007/978-3-030-85053-1_3","title":"A Logistic Growth Model with Logistically Varying Carrying Capacity for Covid-19 Deaths Using Data from Ontario, Canada","year":2021,"lang":"en","type":"book-chapter","venue":"Fields Institute communications","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Logistic regression; Coronavirus disease 2019 (COVID-19); Logistic function; Negative binomial distribution; Logistic distribution; Growth model; Econometrics; Binomial regression; Statistics; Infectious disease (medical specialty); Environmental science; Geography; Disease; Mathematics; Medicine; Poisson distribution","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006010277,0.0006017499,0.001189834,0.00007382048,0.001379892,0.00008351392,0.003212978,0.000587219,0.000177991],"category_scores_gemma":[0.01468734,0.0005431156,0.0001553122,0.00007023361,0.0007595931,0.0001830852,0.003450376,0.001473915,0.000001854374],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003414631,"about_ca_system_score_gemma":0.008112302,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9252963,"about_ca_topic_score_gemma":0.9967011,"domain_scores_codex":[0.9971029,0.0001148455,0.000965466,0.0009631446,0.0003884917,0.0004651649],"domain_scores_gemma":[0.9824017,0.01068295,0.0005847116,0.00563313,0.0003854791,0.0003120106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003928402,0.00006582232,0.0001522638,0.0003514047,0.000649544,0.00004522973,0.0002994817,0.007093285,0.000002531728,0.9723025,0.01893538,0.00006330699],"study_design_scores_gemma":[0.0005369909,0.00003810621,0.000009678144,0.0006036391,0.001277004,0.00001744907,0.00004171521,0.07865264,7.741127e-7,0.7556468,0.1622651,0.0009101761],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003918229,0.0005974998,0.9317422,0.005986009,0.0002084234,0.0009778104,0.004275881,0.0001325406,0.05604048],"genre_scores_gemma":[0.02422889,0.0006046311,0.9461103,0.007358273,0.0002002147,0.0002114747,0.00513518,0.0001121841,0.01603884],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2166557,"threshold_uncertainty_score":0.9999202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7763850100306459,"score_gpt":0.4582054423377914,"score_spread":0.3181795676928544,"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."}}