{"id":"W2982604179","doi":"10.3389/fmars.2019.00653","title":"Perspectives on in situ Sensors for Ocean Acidification Research","year":2019,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Ocean Acidification Effects and Responses","field":"Earth and Planetary Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Tula Foundation; Fisheries and Oceans Canada; University of Victoria; University of Calgary; Ocean Networks Canada Society","funders":"Hakai Institute","keywords":"Metadata; Quality assurance; Limiting; Ocean acidification; Software deployment; Reliability (semiconductor); Computer science; Data quality; Ocean observations; Process (computing); Best practice; Quality (philosophy); Control (management); Data science; Environmental science; Environmental resource management; Process management; Oceanography; Engineering; Operations management; Geography; World Wide Web; Software engineering; Seawater; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003202617,0.00008776382,0.0001293882,0.0009446298,0.0001561494,0.00008755214,0.0004463626,0.00004178927,0.0001147606],"category_scores_gemma":[0.0006183963,0.00007047573,0.00002597792,0.001532136,0.0004444915,0.0002948758,0.00002697355,0.0002001778,0.00009719538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005187672,"about_ca_system_score_gemma":0.0001393115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009606441,"about_ca_topic_score_gemma":0.00005384306,"domain_scores_codex":[0.9982678,0.0001514178,0.0001613775,0.0005160075,0.0004727294,0.0004306564],"domain_scores_gemma":[0.9991819,0.00030266,0.00004186609,0.0002931094,0.000102761,0.00007767624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002770986,0.00004864581,0.9666606,0.00001802956,0.000001510103,0.000002429633,0.001341615,0.002942367,0.001451704,0.0009599734,0.0008152509,0.02548084],"study_design_scores_gemma":[0.0003279485,0.0002142287,0.9754837,0.00001665897,6.309429e-7,0.000001029427,0.005834114,0.01352876,0.001658683,0.001715877,0.001105169,0.0001132588],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857008,0.00004745968,0.00006745318,0.0007161893,0.0005120991,0.0006092292,0.000006801022,0.00001591544,0.01232406],"genre_scores_gemma":[0.9899823,0.00005365336,0.008435315,0.00003168495,0.00003664403,0.000001857137,0.00001078234,0.000002885641,0.001444897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02536758,"threshold_uncertainty_score":0.2873917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124925891866127,"score_gpt":0.2884639900354444,"score_spread":0.2672147311167832,"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."}}