{"id":"W2986407659","doi":"10.1109/igarss.2019.8898032","title":"Applying Machine Learning to Earth Observations In A Standards Based Workflow","year":2019,"lang":"en","type":"article","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Workflow; Computer science; Software deployment; Python (programming language); Artificial intelligence; Discriminative model; Documentation; Machine learning; Reuse; Data science; Deep learning; Earth observation; Software engineering; Software; Database; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01368231,0.0008139733,0.0007036469,0.002777779,0.00111393,0.005985386,0.002462425,0.001219271,0.003447779],"category_scores_gemma":[0.02470293,0.0007666182,0.002024632,0.003108228,0.001746428,0.004288127,0.004002361,0.002447044,0.002814442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002507568,"about_ca_system_score_gemma":0.006165344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01394396,"about_ca_topic_score_gemma":0.009121438,"domain_scores_codex":[0.994863,0.001474095,0.0009329749,0.001008285,0.001443741,0.0002778603],"domain_scores_gemma":[0.9841788,0.005182218,0.0007295081,0.006774904,0.002659515,0.0004750174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003867022,0.0006302499,0.01168179,0.0004088306,0.0002516005,0.0007874189,0.001735491,0.1584624,0.0126268,0.1942535,0.02397857,0.5947966],"study_design_scores_gemma":[0.00008490041,0.00007656323,0.001886788,0.0001734681,0.00004115822,0.0001352967,0.0004890261,0.6092545,0.02670996,0.2875777,0.07346981,0.000100707],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004353039,0.00004181183,0.9796165,0.0009569816,0.00005318458,0.0003368477,0.0006563853,0.01091784,0.003067409],"genre_scores_gemma":[0.07060134,0.0001758615,0.9228237,0.0002941003,0.00003988883,0.000436953,0.002541403,0.001041451,0.002045267],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01394396,"threshold_uncertainty_score":0.07235992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1260302748384711,"score_gpt":0.3707784165262545,"score_spread":0.2447481416877834,"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."}}