{"id":"W6910866301","doi":"10.48668/vt8ump/rk7s2t","title":"edible.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":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["open_science","research_integrity"],"category_scores_codex":[0.01443926,0.001018484,0.001200757,0.003307793,0.001591024,0.001842268,0.01930065,0.00145656,0.0003387847],"category_scores_gemma":[0.008960778,0.001011332,0.0002306054,0.004239721,0.002016156,0.001535507,0.02454127,0.007574533,0.4312952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00132552,"about_ca_system_score_gemma":0.003795489,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01755553,"about_ca_topic_score_gemma":0.001868835,"domain_scores_codex":[0.979539,0.003466285,0.001387833,0.004284312,0.008037369,0.003285252],"domain_scores_gemma":[0.9694653,0.002474143,0.0004994445,0.02491824,0.001364406,0.001278484],"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":[0.0001620295,0.0003598642,0.00001455739,0.0009855331,0.0004233123,0.005222079,0.00001321113,0.000001299859,0.001748359,0.000007469186,0.9908844,0.0001778683],"study_design_scores_gemma":[0.0005089621,0.0001861697,0.00009876188,0.0007910457,0.0001454976,0.0002446351,0.0001406145,0.00004988875,0.0004579874,0.00004474064,0.9963972,0.0009345184],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001613438,0.001755034,0.000001735694,0.0001690633,0.004807603,0.00155209,0.9888017,0.0008355057,0.002061199],"genre_scores_gemma":[0.000001498153,0.001572314,0.00008826495,0.0000332458,0.008032849,0.0003614362,0.9686156,0.0005683959,0.02072638],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4309565,"threshold_uncertainty_score":0.9998398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5530324821035331,"score_gpt":0.5411763995475676,"score_spread":0.01185608255596549,"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."}}