{"id":"W6968056778","doi":"10.5281/zenodo.13324359","title":"StratMC","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Python (programming language); Probabilistic logic; Documentation; Toolbox; Markov chain Monte Carlo; Proxy (statistics); Bayesian probability; Markov chain","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":[0.001703442,0.001840133,0.001531017,0.001790809,0.001108784,0.003541095,0.003700074,0.001571568,0.165857],"category_scores_gemma":[0.008989956,0.001484917,0.002602288,0.002089313,0.0006102567,0.003404545,0.003013004,0.002093421,0.0755688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001123498,"about_ca_system_score_gemma":0.002738521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01488141,"about_ca_topic_score_gemma":0.01946027,"domain_scores_codex":[0.9990547,0.0002216275,0.00005275196,0.0002424592,0.0003030555,0.0001253184],"domain_scores_gemma":[0.9980989,0.0008142605,0.000112563,0.0004230242,0.000431351,0.0001199654],"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":[0.0002166602,0.00006950615,0.003911068,0.0009689373,0.0004205881,0.0002968705,0.0003028955,0.04961164,0.001604034,0.06024599,0.7781301,0.1042218],"study_design_scores_gemma":[0.0002360986,0.00004515403,0.001935306,0.0002379949,0.0001449062,0.0003010331,0.00008148122,0.1927438,0.0035383,0.09737354,0.7032044,0.0001580436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00570786,0.001145074,0.4435073,0.001408376,0.0007120736,0.0003111769,0.2116989,0.2783625,0.05714689],"genre_scores_gemma":[0.07544052,0.001473105,0.4411511,0.001544209,0.0004172904,0.002060093,0.2791443,0.1581281,0.04064118],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.165857,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03904972998336357,"score_gpt":0.2636816355747664,"score_spread":0.2246319055914028,"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."}}