{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002418955,0.00108408,0.0003654348,0.001365463,0.001517587,0.003832939,0.001702063,0.0008454605,0.01811765],"category_scores_gemma":[0.005798567,0.0003087057,0.000695751,0.0006456514,0.001058527,0.001421418,0.004012698,0.001145276,0.005051848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008042215,"about_ca_system_score_gemma":0.001086225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003237517,"about_ca_topic_score_gemma":0.009876698,"domain_scores_codex":[0.9982055,0.001140176,0.00006109802,0.0001502955,0.0002368601,0.000206154],"domain_scores_gemma":[0.9971908,0.001325364,0.0001044971,0.0003786593,0.0002615907,0.0007390691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007779838,0.001270996,0.01146571,0.001105812,0.00009083343,0.002093619,0.03523609,0.01118932,0.01784828,0.04409141,0.1697582,0.7050717],"study_design_scores_gemma":[0.0001931142,0.0008343096,0.01853479,0.001237888,0.00006161092,0.001628281,0.01175853,0.01680491,0.007100957,0.03123177,0.9103353,0.0002784964],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1804888,0.001415335,0.4206298,0.006379377,0.001488026,0.00304976,0.002338106,0.01510016,0.3691107],"genre_scores_gemma":[0.3784441,0.001646287,0.5586123,0.001142948,0.0003680601,0.002427566,0.001917711,0.002006176,0.05343486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01811765,"threshold_uncertainty_score":0.06060952,"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."}}