{"id":"W4400884871","doi":"10.2196/54590","title":"Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data warehouse; Metadata; Identifier; Data science; Pipeline (software); Software; USable; Database; Reuse; Data transformation; Feature (linguistics); Adaptation (eye); World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.01672464,0.0002419458,0.0004006039,0.000171423,0.0003714885,0.001740549,0.03164914,0.0001530452,0.0005336176],"category_scores_gemma":[0.02948405,0.0001256296,0.00002603251,0.001047537,0.0003839353,0.004083406,0.06681766,0.0007796707,0.001134235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002257385,"about_ca_system_score_gemma":0.0003357497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003031682,"about_ca_topic_score_gemma":0.003497436,"domain_scores_codex":[0.9944894,0.0002806554,0.001242096,0.0007084123,0.002835266,0.0004441322],"domain_scores_gemma":[0.9698323,0.003219156,0.0001968204,0.02601164,0.0001719651,0.0005681091],"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":[0.00001134348,0.00003743469,0.00001042633,0.00005640604,0.00004734014,0.00001443743,0.001098567,0.000002487715,2.091138e-7,0.002931213,0.9290878,0.06670236],"study_design_scores_gemma":[0.0002142777,0.00002016358,0.00009392989,0.0001046145,0.00003662637,0.0000210203,0.002513073,0.2199667,2.781666e-7,0.001079804,0.775809,0.0001405611],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003206859,0.0009556552,0.2074269,0.2816388,0.00141645,0.001246322,0.5053381,0.0002739169,0.001383221],"genre_scores_gemma":[0.02935247,0.004257473,0.04839749,0.136432,0.005184591,0.000166365,0.766108,0.0001342513,0.00996732],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.26077,"threshold_uncertainty_score":0.9996435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2784364691961687,"score_gpt":0.4669328239810727,"score_spread":0.1884963547849041,"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."}}