{"id":"W6902487142","doi":"10.7281/t1/bmathh/kd6zip","title":"22_D2.7z.088","year":2021,"lang":"en","type":"dataset","venue":"Research Data Repository, Duke University","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Process (computing); Compression (physics); Object (grammar)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.004482231,0.0007952618,0.001051731,0.002689348,0.002415662,0.0009067278,0.01433309,0.001190098,0.0008855719],"category_scores_gemma":[0.002012563,0.0009549221,0.000279534,0.004105144,0.001499763,0.001837627,0.02186495,0.005189321,0.009724259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003276735,"about_ca_system_score_gemma":0.004919623,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01352249,"about_ca_topic_score_gemma":0.002469123,"domain_scores_codex":[0.983877,0.004996486,0.0005529977,0.003638359,0.004853098,0.002082056],"domain_scores_gemma":[0.9788292,0.001117093,0.0004104009,0.01656867,0.001879652,0.001194973],"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":[0.0003204138,0.0006211817,0.00007118734,0.0003927871,0.0005331549,0.023504,0.00001647496,0.000002100726,0.0009685699,0.00009065345,0.9734017,0.00007781098],"study_design_scores_gemma":[0.0008331264,0.0001116882,0.000241672,0.0003897015,0.0002943739,0.0003450765,0.0005681753,0.0000477874,0.0002870882,0.000004623683,0.9960036,0.0008730365],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001696624,0.0008057053,0.000006768736,0.0001439332,0.0009909836,0.0008072243,0.9859729,0.0002342441,0.01086862],"genre_scores_gemma":[0.00004791901,0.001396391,0.0001983793,0.00001839634,0.001167997,0.00000234783,0.9721869,0.0001300906,0.02485156],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02315892,"threshold_uncertainty_score":0.9992901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2198330705627614,"score_gpt":0.3997847058436866,"score_spread":0.1799516352809253,"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."}}