{"id":"W1993261590","doi":"10.1108/07378830710820943","title":"Information visualization and large‐scale repositories","year":2007,"lang":"en","type":"article","venue":"Library Hi Tech","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Visualization; Information visualization; Leverage (statistics); Data visualization; Data science; USable; Interface (matter); Scale (ratio); User interface; Context (archaeology); Visual analytics; Scatter plot; Creative visualization; Human–computer interaction; Information retrieval; World Wide Web; Data mining; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.007654641,0.001013306,0.0006889146,0.006523386,0.00186906,0.01280674,0.00214537,0.002006781,0.01419836],"category_scores_gemma":[0.0356612,0.0006312883,0.001151201,0.01041844,0.00360792,0.01548022,0.007359567,0.002433604,0.002031268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002688664,"about_ca_system_score_gemma":0.002802575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003910421,"about_ca_topic_score_gemma":0.002838671,"domain_scores_codex":[0.9938536,0.003646521,0.0003817318,0.0004789784,0.001387544,0.0002516492],"domain_scores_gemma":[0.9748799,0.01743293,0.001546711,0.003028741,0.002352477,0.0007593841],"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":[0.0002388127,0.0001497706,0.004382607,0.00169825,0.0001047891,0.000768668,0.02381761,0.0101268,0.002748652,0.6444842,0.04816787,0.263312],"study_design_scores_gemma":[0.00009763424,0.0001148584,0.002626373,0.001527466,0.00007597746,0.00149725,0.008312872,0.03503452,0.005686689,0.4014408,0.5433999,0.0001856025],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02011882,0.003843937,0.9088927,0.01111236,0.0005571208,0.0004742935,0.0009990331,0.01412792,0.03987377],"genre_scores_gemma":[0.2553235,0.003393423,0.7181942,0.001792799,0.0003368298,0.001117994,0.001450585,0.003229495,0.01516111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01419836,"threshold_uncertainty_score":0.04749823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00623848272861038,"score_gpt":0.2545144441872378,"score_spread":0.2482759614586275,"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."}}