{"id":"W4285818913","doi":"10.3133/fs20223053","title":"North Dakota and Landsat","year":2022,"lang":"en","type":"article","venue":"Fact sheet","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geography; Waterfowl; Geological survey; Wetland; Archaeology; Habitat; Geology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000589232,0.00004579913,0.00004449055,0.00001007557,0.0001606644,0.00001306203,0.00008871156,0.000005012228,0.007503884],"category_scores_gemma":[0.0000018419,0.00003855858,0.00001281031,0.00006291932,0.00001984035,0.00004214911,0.0003321521,0.00004927386,0.0002081753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000251091,"about_ca_system_score_gemma":9.161918e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001375968,"about_ca_topic_score_gemma":0.0001188279,"domain_scores_codex":[0.9995654,0.00001665825,0.00004575704,0.0001257323,0.0001417172,0.0001047958],"domain_scores_gemma":[0.9998456,0.000007088509,0.0000131729,0.00009616776,2.992389e-7,0.00003771319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00000393669,0.00001920229,0.9099318,0.000001499479,0.000003263237,0.000009339303,0.0001455335,0.0007592217,0.00001464822,0.00001400892,0.08298245,0.00611509],"study_design_scores_gemma":[0.0001024103,0.0000264302,0.5757464,2.60149e-7,0.00000249969,0.000001970822,0.00004099753,0.0002027529,0.00000256427,0.00004105001,0.4237864,0.00004628393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9550362,0.00001796266,0.00001504355,0.000571039,0.00006947732,0.00006488438,0.00001701374,0.00002080538,0.04418752],"genre_scores_gemma":[0.9955516,0.00001143285,0.00003399915,0.0008027552,0.00001624527,0.00001140606,0.00001496697,0.000003976269,0.003553577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.340804,"threshold_uncertainty_score":0.9934034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005756705742415564,"score_gpt":0.1768122072928356,"score_spread":0.17105550155042,"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."}}