{"id":"W7018127177","doi":"","title":"Deepening digital know-how: building digital talent","year":2015,"lang":"en","type":"other","venue":"Jisc Repository (Jisc)","topic":"","field":"","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Information technology; Digital transformation; Key (lock); Higher education; Work (physics)","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0003358986,0.001747957,0.001583574,0.001124762,0.0003334523,0.0039398,0.001520489,0.001208208,0.0003207829],"category_scores_gemma":[0.0003699642,0.00170329,0.0007245004,0.0007093832,0.0005253633,0.001719456,0.001061274,0.001217426,0.008174594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001299867,"about_ca_system_score_gemma":0.0006509168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001114501,"about_ca_topic_score_gemma":0.00002640761,"domain_scores_codex":[0.9927335,0.0001419843,0.001065698,0.002151954,0.002323212,0.00158369],"domain_scores_gemma":[0.9946103,0.0001259724,0.001617702,0.002323159,0.0003200812,0.001002767],"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":[0.00007347705,0.0002711257,0.001491871,0.0001827166,0.0006801724,0.001356319,0.0002603362,0.00002587745,0.002885574,0.0001013036,0.9908655,0.001805727],"study_design_scores_gemma":[0.001017054,0.0001769856,0.00005323783,0.001298672,0.0002097572,0.0009671274,0.0002533879,0.00008577561,0.001010811,0.00006941785,0.9928258,0.002031959],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00296161,0.007362912,0.0001855524,0.00006368451,0.004324858,0.001174321,0.001350896,0.003330856,0.9792453],"genre_scores_gemma":[0.09266353,0.00001508345,0.0005351828,0.00002322926,0.005409124,0.0001154709,0.0007828909,0.004920851,0.8955346],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.08970192,"threshold_uncertainty_score":0.9995266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01110691605935451,"score_gpt":0.2356157983422058,"score_spread":0.2245088822828512,"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."}}