{"id":"W3022491056","doi":"","title":"Seasonal Mass Balance and Balance Gradients from Airborne Laser Altimetry, Columbia River Basin, Canada.","year":2016,"lang":"en","type":"article","venue":"AGUFM","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Satellite altimetry; Altimeter; Drainage basin; Environmental science; Balance (ability); Climatology; Geology; Hydrology (agriculture); Geography; Remote sensing; Cartography","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":[0.00006024332,0.00016562,0.0001706974,0.000001799309,0.0001203651,0.00002064243,0.0002157911,0.00006533444,0.003036889],"category_scores_gemma":[0.00001647056,0.0001472153,0.00002913243,0.00008971668,0.0003583865,0.0001798386,0.0001746487,0.00007745204,0.0002073757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004403276,"about_ca_system_score_gemma":0.00001945217,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5503719,"about_ca_topic_score_gemma":0.203425,"domain_scores_codex":[0.9986295,0.00003415546,0.0001469865,0.000451566,0.0003629437,0.0003748713],"domain_scores_gemma":[0.9993644,0.00006212733,0.00006988199,0.0002660462,0.000002631153,0.0002349078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001369913,0.00002818078,0.9769617,0.000001621319,0.00001693678,0.00002891296,0.00002496797,0.0004757212,0.000844026,0.000003973066,0.009376985,0.01222324],"study_design_scores_gemma":[0.0006002362,0.00002562431,0.9543999,0.00001769088,0.00001447135,0.000004044944,0.00001874369,0.01033551,0.0001269339,0.0003169971,0.03389085,0.000249003],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960387,0.00006401916,0.001599511,0.0005005141,0.0001832064,0.0001090654,0.0001031113,0.00002505755,0.001376776],"genre_scores_gemma":[0.9836839,0.0001179573,0.006499648,0.001040369,0.00004349826,0.000008725164,0.00001061446,0.00002180543,0.008573457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3469469,"threshold_uncertainty_score":0.9978745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002789159179320122,"score_gpt":0.1580336508064515,"score_spread":0.1552444916271313,"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."}}