{"id":"W2059133370","doi":"10.1089/ind.2012.1528","title":"DELSA Global for “Big Data” and the Bioeconomy: Catalyzing Collective Innovation","year":2012,"lang":"en","type":"article","venue":"Industrial Biotechnology","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Alliance; Big data; Library science; Analytics; Informatics; Political science; Data science; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001341568,0.0001094203,0.0002686849,0.000264318,0.0002302882,0.00004106587,0.0003216414,0.0005833016,0.00001143597],"category_scores_gemma":[0.001128209,0.00009683688,0.0000231441,0.001061904,0.0003467268,0.0002098793,0.0002076409,0.0002372779,0.00004302441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001354132,"about_ca_system_score_gemma":0.00007121589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002661872,"about_ca_topic_score_gemma":0.00002325518,"domain_scores_codex":[0.9988733,0.00001386895,0.0005221725,0.0002706992,0.00001420462,0.0003057527],"domain_scores_gemma":[0.9990578,0.0001093811,0.0003703077,0.0004029371,0.00003969856,0.00001990244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007920137,0.00001851891,0.005407743,0.000002558611,0.00005440963,6.342598e-8,0.00007573998,4.848396e-7,0.00001703882,0.9705613,0.005832696,0.01795026],"study_design_scores_gemma":[0.004125089,0.00005636203,0.0005305952,0.000004670604,0.00001320128,0.0000138036,0.0001406376,0.0003184676,0.001502563,0.1610737,0.8320022,0.0002187685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7611311,0.003525239,0.05412514,0.1321743,0.01279625,0.003512092,0.007033082,0.0003565597,0.02534625],"genre_scores_gemma":[0.9970198,0.00002623433,0.0002997407,0.0008723891,0.001359462,0.00005864308,0.0001353779,0.000009240238,0.000219122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8261694,"threshold_uncertainty_score":0.4498957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2271139611735421,"score_gpt":0.2935812987671387,"score_spread":0.06646733759359663,"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."}}