{"id":"W2791502245","doi":"10.1002/2017gb005783","title":"Carbon: Chlorophyll Ratios and Net Primary Productivity of Subarctic Pacific Surface Waters Derived From Autonomous Shipboard Sensors","year":2018,"lang":"en","type":"article","venue":"Global Biogeochemical Cycles","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Phytoplankton; Subarctic climate; Oceanography; Environmental science; Hydrography; Upwelling; Productivity; Nutrient; Chlorophyll a; Primary production; Biology; Geology; Ecology; Ecosystem; Botany","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002396903,0.0003982482,0.0001505811,0.0007698433,0.0002278216,0.0004633614,0.0001733201,0.0001662382,0.0005591252],"category_scores_gemma":[0.00064044,0.0001931391,0.0002165841,0.0008147633,0.0001588492,0.0002801217,0.0002986857,0.0001699309,0.0001408353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005054158,"about_ca_system_score_gemma":0.0003004808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06821977,"about_ca_topic_score_gemma":0.1122554,"domain_scores_codex":[0.9999337,0.000008004821,0.00000630941,0.00002183867,0.00002006718,0.00001013468],"domain_scores_gemma":[0.9996933,0.00006530643,0.00009558078,0.00001824443,0.0000930235,0.0000345812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003358191,0.0000135704,0.9927785,0.00001465066,0.00004577735,0.00002989077,0.00006839976,0.0008696304,0.00233687,0.00001275839,0.00009544333,0.003701015],"study_design_scores_gemma":[0.000001670239,0.000006159206,0.9981723,0.000002749454,0.000008639081,0.00001298376,0.00005794766,0.001268646,0.0003849256,0.000006189519,0.00007597372,0.000001802717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987099,0.00003737669,0.0001722212,0.000005971504,0.000001539882,0.000002658402,0.0007198442,0.00001200465,0.0003385187],"genre_scores_gemma":[0.9978657,0.00006308337,0.0006195406,0.00001036017,0.000002837313,0.000006876859,0.001293778,0.0000042949,0.000133532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06821977,"threshold_uncertainty_score":0.1356454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007856946104365808,"score_gpt":0.1861081652879207,"score_spread":0.1782512191835549,"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."}}