{"id":"W6948476431","doi":"10.5064/f6gzgcjb/nxjsjw","title":"Lum_53076.Patient_SC_2018.01.25_Alberta.pdf","year":2022,"lang":"ru","type":"dataset","venue":"Syracuse University Qualitative Data Repository","topic":"Subterranean biodiversity and taxonomy","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Nursing Research; National Institutes of Health","keywords":"Process (computing); Identification (biology); Product (mathematics)","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","sts","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001986702,0.001152708,0.001258827,0.0009074397,0.004100578,0.0003454158,0.007289016,0.0006886347,0.3091396],"category_scores_gemma":[0.0003490014,0.001350725,0.0004436988,0.001058018,0.00152046,0.002891271,0.002992946,0.002111852,0.01236393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003554308,"about_ca_system_score_gemma":0.0009261209,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05418512,"about_ca_topic_score_gemma":0.008527279,"domain_scores_codex":[0.9877855,0.005400144,0.000913909,0.002916252,0.001749527,0.001234699],"domain_scores_gemma":[0.9904598,0.003049302,0.001521947,0.003906114,0.0002022655,0.0008605666],"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":[0.001110497,0.000435528,0.003196398,0.0002861307,0.001002793,0.002157426,0.008388804,0.00003866295,0.000003771873,0.00003263372,0.9829884,0.0003590233],"study_design_scores_gemma":[0.001450577,0.0007154663,0.001294826,0.0001035766,0.0009511919,0.00009146652,0.07377404,0.0001616829,0.000007685127,0.00001639708,0.9198905,0.001542575],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007435873,0.0004748745,0.00003308358,0.0003228528,0.004296737,0.0009594508,0.9693009,0.0001029002,0.01707335],"genre_scores_gemma":[0.001465644,0.0005831539,0.0004753754,0.0003939924,0.0003796058,9.944218e-7,0.9748791,0.00002024562,0.02180181],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2967757,"threshold_uncertainty_score":0.9988942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007344790539275,"score_gpt":0.2656864351061383,"score_spread":0.1649519560522109,"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."}}