{"id":"W4394071307","doi":"10.6084/m9.figshare.20415819","title":"Malaise_trap_ReadME.txt","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Trap (plumbing); Malaise; Geography; Meteorology; Medicine","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00004764411,0.0002908209,0.0001984391,0.00007907266,0.00006503834,0.00005869872,0.0005994509,0.000579341,0.0588871],"category_scores_gemma":[0.001296962,0.0002832885,0.0001464119,0.0001100599,0.000008498784,0.000001681448,0.000453118,0.0003791829,0.1057542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000113728,"about_ca_system_score_gemma":0.0001147409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001573198,"about_ca_topic_score_gemma":0.00004748566,"domain_scores_codex":[0.9989131,0.00003176418,0.0002405084,0.0003057828,0.0002108027,0.0002980035],"domain_scores_gemma":[0.9988363,0.00002672519,0.0001848078,0.0007856922,0.0000707611,0.00009570208],"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":[0.000005229772,0.00001064634,9.440068e-7,0.0004966481,0.0000453557,0.00001292568,0.000001585483,0.00002281615,0.00001538261,1.296532e-7,0.9992219,0.0001664339],"study_design_scores_gemma":[0.0001201966,0.00008267253,0.00006478517,0.0004827048,0.00001523172,0.00001702566,0.000002619852,0.00002223998,0.000124206,0.000001248423,0.9987401,0.0003269883],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00000193873,0.00009521099,2.54223e-7,0.0000192105,0.0001625625,0.0001901178,0.9986244,0.00005501048,0.0008512552],"genre_scores_gemma":[0.000001525041,0.00005915536,0.00004511683,0.0003276694,0.0005967626,0.0001426883,0.9965892,0.00004862001,0.002189308],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04686708,"threshold_uncertainty_score":0.9999619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02037946096692176,"score_gpt":0.2855029548047442,"score_spread":0.2651234938378225,"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."}}