{"id":"W7006505456","doi":"","title":"Using AToM3 as a Meta-CASE Tool","year":2015,"lang":"en","type":"article","venue":"Biblos-e Archivo (Universidad Autónoma de Madrid)","topic":"Biological and pharmacological studies of plants","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Information system; Information technology; Process (computing); Automation; Identification (biology); Work (physics); Key (lock)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003685025,0.0003237422,0.000663698,0.000316971,0.0002768856,0.00003655235,0.0002368037,0.0001655127,0.001324619],"category_scores_gemma":[0.0001661015,0.0002321567,0.0003874403,0.0007108303,0.0002170715,0.0001415015,0.0003924229,0.0004124995,0.0003673567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000148144,"about_ca_system_score_gemma":0.0001630975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000543556,"about_ca_topic_score_gemma":0.00002191125,"domain_scores_codex":[0.998108,0.0001831352,0.0002494614,0.0004848606,0.0003115569,0.0006629298],"domain_scores_gemma":[0.9985844,0.0002224379,0.0001130788,0.0002745178,0.00009880675,0.0007067316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01604393,0.005973496,0.1131827,0.0007350336,0.02390744,0.4289492,0.004987802,0.0004461524,0.09368843,0.05076206,0.1347186,0.1266051],"study_design_scores_gemma":[0.02479668,0.00779524,0.1136953,0.0002329036,0.01972423,0.07526032,0.005959988,0.0161493,0.006766966,0.04400817,0.6819246,0.003686281],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9627642,0.001061633,0.0005568495,0.002453425,0.0001369325,0.0004406368,0.00006899171,0.0002150033,0.03230235],"genre_scores_gemma":[0.9849067,0.00009842376,0.01010172,0.002623719,0.0002325257,0.00001279832,0.00002088596,0.00002172179,0.001981555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.547206,"threshold_uncertainty_score":0.9995883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2058433224808192,"score_gpt":0.3514690432883415,"score_spread":0.1456257208075223,"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."}}