{"id":"W2093458466","doi":"10.5589/m09-032","title":"Remote predictive mapping of the Boothia mainland area, Nunavut, Canada: an iterative approach using Landsat ETM, aeromagnetic, and geological field data","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thematic Mapper; Geology; Geologic map; Remote sensing; Bedrock; Lithology; Scale (ratio); Thematic map; Foreland basin; Cartography; Tectonics; Seismology; Geomorphology; Geography; Satellite imagery; Geochemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006355887,0.0001669756,0.0002997172,0.00009683849,0.000280021,0.0001049847,0.0007852636,0.0001170183,0.000004189621],"category_scores_gemma":[0.0004939534,0.0001232372,0.00004031811,0.0002666541,0.0001083196,0.0002639554,0.0001179319,0.0004316113,3.691156e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001100397,"about_ca_system_score_gemma":0.001499237,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1471982,"about_ca_topic_score_gemma":0.2259207,"domain_scores_codex":[0.9985647,0.0001625592,0.000375391,0.0003068888,0.0002291564,0.0003613296],"domain_scores_gemma":[0.9983152,0.0001136058,0.0003413076,0.00059926,0.0002545288,0.0003761627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001561095,0.00004398795,0.01217552,0.0001809458,0.0003179766,0.003507014,0.01623088,0.01187967,0.008625519,0.0004059632,0.006430065,0.9400464],"study_design_scores_gemma":[0.0003316097,0.0001725716,0.01335819,0.0003290452,0.00003168262,0.003368319,0.0005850753,0.9746268,0.0007624946,0.002183451,0.004032739,0.0002180108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5051522,0.0004917059,0.4806489,0.008359899,0.0003222262,0.0001650743,0.00001482962,0.000009447992,0.004835721],"genre_scores_gemma":[0.852478,0.000008118276,0.1466393,0.0007133132,0.000108182,2.775713e-9,0.000002866171,0.000002349034,0.00004792694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9627472,"threshold_uncertainty_score":0.8584806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03335881626116587,"score_gpt":0.2190135280973063,"score_spread":0.1856547118361404,"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."}}