{"id":"W4399587825","doi":"10.32614/cran.package.cchsflow","title":"cchsflow: Transforming and Harmonizing CCHS Variables","year":2020,"lang":"en","type":"dataset","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.00020508,0.0002862215,0.0004370543,0.000119234,0.0002203558,0.0005770512,0.0008703097,0.0001553433,0.0001259756],"category_scores_gemma":[0.00004161622,0.000242087,0.0001155869,0.000397796,0.00003519557,0.0004592363,0.0004340203,0.000316706,0.00009180603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001732336,"about_ca_system_score_gemma":0.00006017881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002607746,"about_ca_topic_score_gemma":0.00009467015,"domain_scores_codex":[0.9984103,0.00002945245,0.0003522869,0.0006225303,0.0002569113,0.0003284553],"domain_scores_gemma":[0.9991307,0.00008559447,0.0001241359,0.000450572,0.00003688161,0.0001720882],"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":[0.000001422408,0.000009032444,0.00000123793,0.00008032801,0.00005891391,0.00003030958,0.00004530484,0.00000734499,0.00001831148,0.001262949,0.9847226,0.01376221],"study_design_scores_gemma":[0.0001091569,0.00004081606,0.000001506224,0.00004985625,0.00006572747,0.00002918869,0.00002633773,0.0166283,0.00005067896,0.0001977489,0.9824927,0.0003079973],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002474939,0.0004160611,0.192778,0.0006383626,0.0002296929,0.0001356202,0.8048618,0.0001483414,0.0007896605],"genre_scores_gemma":[0.00005659578,0.0008697422,0.0638239,0.001089858,0.0003758665,0.00001309925,0.9334591,0.00002132332,0.0002905275],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1289541,"threshold_uncertainty_score":0.987202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03176814484856359,"score_gpt":0.2172745112294149,"score_spread":0.1855063663808513,"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."}}