{"id":"W2557966447","doi":"10.4043/27329-ms","title":"Arctic Monitoring: A Remote Sensing Analysis of Former Wellsites","year":2016,"lang":"en","type":"article","venue":"Arctic Technology Conference","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Imperial Oil (Canada)","funders":"","keywords":"Remote sensing; Normalized Difference Vegetation Index; Arctic; Vegetation (pathology); Synthetic aperture radar; Environmental science; Shore; Radar; Physical geography; Computer science; Geography; Geology; Climate change; Oceanography; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0001968943,0.0001529852,0.0003242338,0.0004649238,0.00007618694,0.00002121802,0.000821323,0.0001845845,0.0000534837],"category_scores_gemma":[0.0005441394,0.000112436,0.000103956,0.001629142,0.0002869811,0.0001481726,0.0003775449,0.0001482627,0.00002331058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005730738,"about_ca_system_score_gemma":0.00005467435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001237953,"about_ca_topic_score_gemma":0.00001173075,"domain_scores_codex":[0.9987099,0.00002943324,0.0002884217,0.0004586319,0.0001557186,0.0003578541],"domain_scores_gemma":[0.9982017,0.0001842728,0.0002018961,0.0009553694,0.0004048833,0.00005192978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001374902,0.00005220942,0.1082998,0.00009880608,0.0009516884,0.00006006615,0.0004642244,0.00002213445,0.2509027,0.1066857,0.00001600299,0.5324329],"study_design_scores_gemma":[0.0009161187,0.0002250406,0.0462075,0.0007031383,0.0006022913,0.0001593131,0.0005049575,0.0365292,0.5070334,0.4030932,0.003200052,0.0008257972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2505603,0.00006184627,0.7290525,0.01721015,0.0001377492,0.00008156984,0.000001218611,0.0003085156,0.002586177],"genre_scores_gemma":[0.9562939,0.00003424646,0.0429824,0.00003004581,0.00001154653,9.788038e-7,4.583457e-7,0.000002101694,0.0006442671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7057337,"threshold_uncertainty_score":0.4585006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01990326839143848,"score_gpt":0.2385483162522845,"score_spread":0.218645047860846,"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."}}