{"id":"W4413002757","doi":"10.3997/1365-2397.fb2025060","title":"The use of Gaming and Geodata Visualisation in Preparation for High Arctic Research Fieldwork","year":2025,"lang":"en","type":"article","venue":"First Break","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Northern Studies; Université de Sherbrooke","funders":"","keywords":"Telmatology; Visualization; Metamorphic petrology; Arctic; Geology; Regional geology; Environmental geology; Prospection; Palaeogeography; Glaciology; Economic geology; The arctic; Geochemistry; Physical geography; Earth science; Data science; Paleontology; Computer science; Oceanography; Tectonics; Data mining; Geography; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001665654,0.00002824218,0.00006425331,0.00009452893,0.0007293138,0.00009212892,0.00007161484,0.00004324042,0.000003449189],"category_scores_gemma":[0.00167071,0.00002306638,0.00001267516,0.00040651,0.000140971,0.0002318335,0.0000488407,0.00004570757,6.179097e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003463864,"about_ca_system_score_gemma":0.0000517446,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01762447,"about_ca_topic_score_gemma":0.0640984,"domain_scores_codex":[0.999315,0.0001201194,0.0001905095,0.00007270063,0.0001751534,0.000126472],"domain_scores_gemma":[0.9976361,0.001895062,0.00005487233,0.0001081048,0.0002963651,0.00000949847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000970849,0.00003752915,0.2784061,0.0003395535,0.00005085574,2.340617e-7,0.09214588,0.0001194925,0.00001082286,0.6099873,0.01269389,0.006111288],"study_design_scores_gemma":[0.0005987438,0.00006244304,0.4703963,0.0005535364,0.00001368939,3.457539e-7,0.04331901,0.001063505,0.00008967836,0.01712046,0.466662,0.0001202654],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840717,0.0004621345,0.001242065,0.009401298,0.0004419385,0.001430537,0.0000164241,0.00002158109,0.00291228],"genre_scores_gemma":[0.9987242,0.0001610721,0.0001363285,0.00002595239,0.00002798286,0.00008696469,0.000009030809,0.000001120839,0.0008274005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5928668,"threshold_uncertainty_score":0.9889172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1449440153054504,"score_gpt":0.423017852632818,"score_spread":0.2780738373273677,"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."}}