{"id":"W2061741415","doi":"10.2478/amsc-2014-0022","title":"Application of GIS Methods in Assessing Effects of Mining Activity on Surface Infrastructure/Zastosowanie Metod Gis W Ocenie Wpływu Działalności Górniczej Na Infrastrukturę Na Powierzchni","year":2014,"lang":"en","type":"article","venue":"Archives of Mining Sciences","topic":"Geotechnical and Mining Engineering","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Politechnika Wrocławska","keywords":"Displacement (psychology); Data mining; Subsidence; Curvature; Field (mathematics); Geographic information system; Tilt (camera); Deformation (meteorology); Process (computing); Surface (topology); Geodesy; Geography; Computer science; Geology; Mathematics; Geotechnical engineering; Remote sensing; Mining engineering; Geometry; Geomorphology; Meteorology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009394455,0.0002812886,0.000619815,0.0004283772,0.00007449895,0.00002556512,0.0005840824,0.0001121261,0.000003569433],"category_scores_gemma":[0.0008441191,0.000261405,0.0001162246,0.0007665997,0.0004379039,0.0002597524,0.0001387395,0.0002835343,4.669643e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002755164,"about_ca_system_score_gemma":0.00006172914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005694386,"about_ca_topic_score_gemma":0.000005764495,"domain_scores_codex":[0.9981064,0.0001611128,0.0005479894,0.0004029986,0.0003673351,0.0004141262],"domain_scores_gemma":[0.9963184,0.002919433,0.0002939845,0.0003534924,0.00002639225,0.00008830745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001011677,0.00003249944,0.004432451,0.0003629095,0.00002207438,4.910169e-7,0.001471791,0.315622,0.498585,0.0001819269,0.000004315449,0.1792745],"study_design_scores_gemma":[0.0003638794,0.0003220122,0.1764852,0.0007398346,0.00002805231,0.000003490308,0.0003331349,0.4350024,0.3860026,0.0003893769,0.00004017998,0.000289764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7998097,0.000125969,0.197671,0.00001187574,0.0001550557,0.000145061,0.000003762174,0.00008991823,0.001987705],"genre_scores_gemma":[0.7287648,0.00001017934,0.2711708,0.000004613406,0.00001591709,0.000008009029,0.000001406339,0.00002038926,0.000003959684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1789847,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0091501194094984,"score_gpt":0.2795658027058232,"score_spread":0.2704156832963248,"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."}}