{"id":"W3194032252","doi":"10.14288/1.0401273","title":"Becoming engineers: how students leverage relationships between documents and learning activities","year":2021,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Leverage (statistics); Computer science; Mathematics education; Data science; Knowledge management; World Wide Web; Artificial intelligence; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004610317,0.0000240463,0.0001155912,0.00003809686,0.001144176,0.000656419,0.0001973416,0.00006328608,0.000117612],"category_scores_gemma":[0.0001436988,0.00008294237,0.00003920833,0.0002873002,0.0001859385,0.008252349,0.0001170737,0.0001859296,0.000005764719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004111186,"about_ca_system_score_gemma":0.0000977303,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01291897,"about_ca_topic_score_gemma":0.0117779,"domain_scores_codex":[0.9990855,0.0001898518,0.00007024832,0.0001498425,0.0003400468,0.000164514],"domain_scores_gemma":[0.9995162,0.0001505871,0.00008445591,0.00007312033,0.00006812329,0.0001075184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[9.828429e-7,0.00002258032,0.8139101,0.00002484197,0.00002908207,0.00002391559,0.03315456,0.000009784192,0.00001276556,0.00004205082,0.000971804,0.1517975],"study_design_scores_gemma":[0.0002569016,0.00001090572,0.8861415,0.00005374702,0.000009302719,0.000002097585,0.1055167,0.00002478193,6.386982e-7,0.0001372633,0.007763405,0.00008279012],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953624,0.00005246137,0.0002556691,0.0005696472,0.0000712626,0.00007437544,0.0000189771,0.00004288901,0.003552308],"genre_scores_gemma":[0.9880797,0.0001293067,0.0004652526,0.00004187286,0.00003434945,1.468224e-7,0.00001881115,0.000002976772,0.01122753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1517148,"threshold_uncertainty_score":0.9936541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02072186712020941,"score_gpt":0.2249659799304872,"score_spread":0.2042441128102778,"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."}}