{"id":"W3033884553","doi":"","title":"Hi-Resolution Local Ecological Marine Units","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Esri (Canada)","funders":"","keywords":"Ecology; Environmental science; Geography; Remote sensing; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006727184,0.0001747718,0.0001473501,0.00003829639,0.0002314038,0.00004429342,0.0004502023,0.0002099421,0.0000790481],"category_scores_gemma":[0.0007968915,0.0001536338,0.00002990139,0.0002836096,0.0007175921,0.0001578514,0.0007943522,0.0002631844,0.002546113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000246529,"about_ca_system_score_gemma":0.00001057376,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01234383,"about_ca_topic_score_gemma":0.004620893,"domain_scores_codex":[0.9983749,0.00006986518,0.0003174815,0.0004005242,0.0003314367,0.0005057862],"domain_scores_gemma":[0.9992391,0.0001391947,0.0001284872,0.0003632135,0.00002800416,0.0001019759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007874535,0.000441025,0.9027712,0.00002786713,0.00003467244,0.0001488359,0.000641563,0.01187821,0.01379089,0.0003235617,0.01637691,0.05348655],"study_design_scores_gemma":[0.0002017092,0.0002416827,0.9634451,0.00002864029,0.00001066683,0.00001575008,0.0001541409,0.000685676,0.02023855,0.002064728,0.01264025,0.0002730803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9617738,0.000005094721,0.00008222335,0.0006277557,0.0003451249,0.0001133411,0.000001577033,0.0005719753,0.03647915],"genre_scores_gemma":[0.9939065,0.000005026845,0.005311258,0.0001107252,0.0001924993,0.00001062654,0.000006438544,0.00001527549,0.0004416387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06067395,"threshold_uncertainty_score":0.9982305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03747463496556998,"score_gpt":0.2571585125216552,"score_spread":0.2196838775560853,"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."}}