{"id":"W6966470483","doi":"10.48668/vt8ump/1elgza","title":"sanderia.PacBio.fastq","year":2023,"lang":"en","type":"dataset","venue":"CUHK Research Data Repository","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Queen's University","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","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","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["open_science","research_integrity"],"category_scores_codex":[0.01242385,0.0009674483,0.001177231,0.002472008,0.001538833,0.00184503,0.01634929,0.001352369,0.0003883322],"category_scores_gemma":[0.007687389,0.0009537622,0.0002167853,0.003166683,0.00187564,0.001314054,0.02059635,0.006198615,0.3370325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001196679,"about_ca_system_score_gemma":0.003275366,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01403278,"about_ca_topic_score_gemma":0.001899522,"domain_scores_codex":[0.9813741,0.003639176,0.001307735,0.004018357,0.006728029,0.002932576],"domain_scores_gemma":[0.9723304,0.002238217,0.0004720989,0.02277229,0.001056408,0.001130582],"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":[0.0001977346,0.0003183366,0.000012792,0.0007797204,0.0004368307,0.004392161,0.00001565767,0.000001052703,0.001737761,0.000006092728,0.9919124,0.0001894369],"study_design_scores_gemma":[0.0005851689,0.0001689461,0.0001399919,0.0006268296,0.000135027,0.0002444356,0.0001631258,0.00005161184,0.0003075031,0.00004822144,0.99663,0.0008991868],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002686819,0.001639089,0.000001732669,0.0001329141,0.003751041,0.001491814,0.9902785,0.000756564,0.001921453],"genre_scores_gemma":[0.000005354149,0.001264144,0.00006706254,0.00003069781,0.00532817,0.00032183,0.9767295,0.0005280403,0.01572519],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3366442,"threshold_uncertainty_score":0.9999441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5571518294041781,"score_gpt":0.5380763801755616,"score_spread":0.01907544922861648,"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."}}