{"id":"W2031923091","doi":"10.1029/2006eo350006","title":"Digital Acquisition, Analysis, and Visualization in the Earth sciences","year":2006,"lang":"en","type":"article","venue":"Eos","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cyberinfrastructure; Visualization; Library science; China; Data science; Computer science; Geography; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002184795,0.00003710852,0.00006023841,0.000101143,0.0001203991,0.0001459047,0.00007246236,0.00001663754,0.0002010469],"category_scores_gemma":[0.00001206362,0.00002052601,0.00002668693,0.000837999,0.00006160648,0.0001175422,0.000002752031,0.0000237165,0.00002703609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":2.007855e-7,"about_ca_system_score_gemma":0.000003746009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001253349,"about_ca_topic_score_gemma":0.004885087,"domain_scores_codex":[0.9995393,0.00003558827,0.00008448699,0.000118691,0.0001310743,0.00009085434],"domain_scores_gemma":[0.999869,0.00005108713,0.00001432889,0.00004341544,0.000009487632,0.00001269847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[6.783168e-7,0.000005666933,0.9616092,4.519152e-7,0.000003901049,0.000001155325,0.0000225771,0.03376198,9.573048e-7,0.0001617831,0.00002089066,0.004410782],"study_design_scores_gemma":[0.00002233507,0.00001888529,0.8523254,9.051001e-7,0.00001833081,9.213017e-7,0.00006802522,0.1460324,0.000002860769,0.001186718,0.0002906871,0.0000325224],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876204,0.000252835,0.00363425,0.0003017617,0.000009455645,0.00002444077,0.000009262976,0.00001051228,0.008137111],"genre_scores_gemma":[0.9995254,0.000009679497,0.00008142861,0.0001371132,0.00003512465,2.164789e-7,0.0001022685,2.392224e-7,0.0001084752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1122704,"threshold_uncertainty_score":0.2725993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109012651288497,"score_gpt":0.2222277702552582,"score_spread":0.2113265051264085,"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."}}