{"id":"W4315705841","doi":"10.48550/arxiv.2301.03710","title":"A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Poisson distribution; Constant (computer programming); Gamma process; Econometrics; Generalization; Bayesian probability; Poisson process; Gamma distribution; Poisson regression; Statistics; Range (aeronautics); Computer science; Mathematics; Multicenter AIDS Cohort Study; Compound Poisson process; Engineering; Demography; Medicine; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008557658,0.0003697951,0.0006941903,0.0002484122,0.00010017,0.00003664254,0.0004341005,0.0002496903,0.00002565934],"category_scores_gemma":[0.001917138,0.0003796185,0.0002069693,0.0001896837,0.0001087128,0.00007188287,0.0009373704,0.0004329401,0.00003354906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004317223,"about_ca_system_score_gemma":0.00009033953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004508599,"about_ca_topic_score_gemma":0.0002145528,"domain_scores_codex":[0.9978966,0.0001830399,0.0004277408,0.0009066098,0.0001057193,0.0004803285],"domain_scores_gemma":[0.9962126,0.002624956,0.0003007236,0.0005262375,0.0002219075,0.0001135902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001270763,0.001281618,0.003186218,0.00676688,0.001802856,0.001597231,0.008592736,0.2739338,0.0001083787,0.6845975,0.006727164,0.01013489],"study_design_scores_gemma":[0.000490461,0.00002798912,0.00002037196,0.0003952815,0.0001129705,5.125963e-7,0.0002597309,0.5243152,0.0000165415,0.4741402,0.000008489313,0.0002122506],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04174836,0.00002872271,0.9554462,0.0001164097,0.000293866,0.00177949,0.000231757,0.0001572207,0.0001979486],"genre_scores_gemma":[0.5357055,0.0002103118,0.4563514,0.00009311889,0.0001129154,0.000102633,0.00002990798,0.0001214004,0.007272815],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4990948,"threshold_uncertainty_score":0.9998656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6448778137186387,"score_gpt":0.363454303008753,"score_spread":0.2814235107098857,"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."}}