{"id":"W2332037986","doi":"10.5860/crln.73.1.8686","title":"Supporting tomorrow’s research: Assessing faculty data curation needs at Georgia Tech","year":2012,"lang":"en","type":"article","venue":"College & Research Libraries News","topic":"Research Data Management Practices","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library of Parliament","funders":"","keywords":"Data curation; Georgia tech; Research data; Data science; Library science; Computer science; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01810086,0.0002439047,0.0003379408,0.004164531,0.008866891,0.009156479,0.002135098,0.002496977,0.005517866],"category_scores_gemma":[0.05170553,0.0006237589,0.0003043805,0.005118989,0.002603793,0.005110964,0.005333106,0.00173329,0.002425287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009299344,"about_ca_system_score_gemma":0.03221281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05271377,"about_ca_topic_score_gemma":0.1725982,"domain_scores_codex":[0.9889932,0.004576616,0.0009021005,0.0007114261,0.003040523,0.001775948],"domain_scores_gemma":[0.9309642,0.01988199,0.00634547,0.004651783,0.01646139,0.02169511],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004572538,0.001111155,0.5365705,0.0006203818,0.00008151993,0.001201872,0.1052842,0.0003956292,0.002423724,0.003598017,0.07537799,0.2728777],"study_design_scores_gemma":[0.00006422493,0.0009941572,0.4536224,0.0009003698,0.0001276699,0.0008889524,0.3415465,0.001835468,0.002414421,0.00319085,0.1942663,0.0001486522],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9487848,0.0008214356,0.0008299588,0.02861582,0.00009533469,0.0002269773,0.0004692168,0.0003322618,0.01982406],"genre_scores_gemma":[0.9759012,0.001430157,0.00898092,0.006009323,0.00007639879,0.0002414715,0.0008183948,0.00009597134,0.006445977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9818991,"threshold_uncertainty_score":0.1048139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5631883997298535,"score_gpt":0.5260172157381796,"score_spread":0.03717118399167385,"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."}}