{"id":"W2801517077","doi":"10.3334/ornldaac/1569","title":"ABoVE: Hyperspectral Imagery from AVIRIS-NG, Alaskan and Canadian Arctic, 2017-2019","year":2018,"lang":"en","type":"article","venue":"Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Arctic; Remote sensing; Geology; Environmental science; Geography; Oceanography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003754145,0.001334321,0.0005867492,0.002280703,0.001487884,0.001074094,0.001201237,0.0005210069,0.005638654],"category_scores_gemma":[0.0004865132,0.0002766565,0.0005510781,0.004128555,0.0004913745,0.0006103372,0.0007331724,0.0008902352,0.00449448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0030065,"about_ca_system_score_gemma":0.005576331,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8427869,"about_ca_topic_score_gemma":0.9312934,"domain_scores_codex":[0.9994602,0.00001791988,0.00001663942,0.0001018149,0.0002727273,0.0001306916],"domain_scores_gemma":[0.9992197,0.00001910287,0.00003059491,0.00007857817,0.0005678509,0.00008426365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0004967878,0.0003641443,0.03393707,0.0004681367,0.0002930981,0.0003227857,0.0003641959,0.007680566,0.01181044,0.0006670039,0.890898,0.05269782],"study_design_scores_gemma":[0.0002284442,0.00006298351,0.3783424,0.0003511679,0.0001564485,0.0003135815,0.002265739,0.01411487,0.008758559,0.0007967997,0.5943772,0.0002318251],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04897314,0.0006325583,0.0008707404,0.0002001693,0.0001953786,0.00007674176,0.938402,0.0016559,0.008993391],"genre_scores_gemma":[0.02642494,0.0001813905,0.002107534,0.00005719506,0.00002288247,0.00003428461,0.9689071,0.0001438469,0.002120778],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1572131,"threshold_uncertainty_score":0.3162779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007647417484741002,"score_gpt":0.2292764822024237,"score_spread":0.2216290647176827,"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."}}