{"id":"W1998217476","doi":"10.1007/s10109-011-0160-x","title":"Evaluating the impact of data quantity, distribution and algorithm selection on the accuracy of 3D subsurface models using synthetic grid models of varying complexity","year":2011,"lang":"en","type":"article","venue":"Journal of Geographical Systems","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; University of Guelph","funders":"","keywords":"Inverse distance weighting; Kriging; Interpolation (computer graphics); Grid; Weighting; Algorithm; Computer science; Multivariate interpolation; Sampling (signal processing); Convergence (economics); Data mining; Selection (genetic algorithm); Mathematical optimization; Mathematics; Machine learning; Bilinear interpolation; 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":[0.01306074,0.001143893,0.0007878236,0.001397664,0.000728514,0.001848362,0.0009973329,0.001712183,0.0006023946],"category_scores_gemma":[0.09591303,0.0009718428,0.0009755391,0.00141593,0.001215983,0.00316948,0.001193405,0.0009781795,0.0001260081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001166841,"about_ca_system_score_gemma":0.001176225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01231295,"about_ca_topic_score_gemma":0.008695985,"domain_scores_codex":[0.9948642,0.003047489,0.0006301114,0.0005170992,0.0006935788,0.0002476371],"domain_scores_gemma":[0.7885134,0.1965232,0.004826621,0.004688785,0.004744221,0.0007039107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002565422,0.0003765752,0.05830744,0.0001412191,0.000335446,0.0001274293,0.0001427956,0.9043247,0.004450565,0.0006088766,0.0003217448,0.02829788],"study_design_scores_gemma":[0.0001890671,0.0005658317,0.009996637,0.00001719866,0.0001552997,0.00006085744,0.00007745598,0.9809664,0.007288856,0.0005223359,0.0001219533,0.00003797238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823958,0.000324639,0.01601074,0.0002991642,0.00003093032,0.00004052691,0.0002763027,0.0002227435,0.0003990593],"genre_scores_gemma":[0.990599,0.0001058557,0.008781468,0.00002931658,0.00001458291,0.00001677618,0.0002826607,0.00007239079,0.00009801613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01306074,"threshold_uncertainty_score":0.06907266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3319342614153623,"score_gpt":0.3587117508519014,"score_spread":0.02677748943653918,"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."}}