{"id":"W2069887166","doi":"10.1016/j.ijggc.2014.09.015","title":"Fiber refractometer to detect and distinguish carbon dioxide and methane leakage in the deep ocean","year":2014,"lang":"en","type":"article","venue":"International journal of greenhouse gas control","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Korea Institute of Geoscience and Mineral Resources; Carbon Management Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Methane; Carbon dioxide; Refractometer; Leakage (economics); Seawater; Optical fiber; Refractive index; Supercritical carbon dioxide; Materials science; Refractometry; Environmental science; Chemistry; Optics; Geology; Optoelectronics; Oceanography; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003312202,0.0004561765,0.0003220107,0.0005860737,0.0003502682,0.0002906196,0.0006057866,0.0005382744,0.001149562],"category_scores_gemma":[0.0004064919,0.0002404296,0.0002426122,0.000494267,0.0001938193,0.0004731385,0.0004352564,0.0005000834,0.000460053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005345471,"about_ca_system_score_gemma":0.0006966783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008027175,"about_ca_topic_score_gemma":0.01310944,"domain_scores_codex":[0.9995561,0.00004691191,0.00001723027,0.000108386,0.0002337819,0.00003744069],"domain_scores_gemma":[0.9996742,0.00007263654,0.00005026835,0.00003184393,0.0001436857,0.00002734043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004422933,0.0001228585,0.0149409,0.0001089394,0.00007145444,0.00007109324,0.00006401593,0.0006461383,0.9423699,0.0002124064,0.0008958614,0.04005416],"study_design_scores_gemma":[0.0001742974,0.00111476,0.08443196,0.0000353865,0.0002357727,0.0008557925,0.0001583966,0.03589868,0.8615178,0.0002488864,0.01520151,0.0001267546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9036769,0.002178174,0.0852996,0.000285128,0.0002276693,0.000157012,0.001702384,0.00149898,0.004974236],"genre_scores_gemma":[0.9318262,0.0009774435,0.06067766,0.0003525099,0.0000540593,0.00008656581,0.0006630076,0.0000587998,0.005303717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008027175,"threshold_uncertainty_score":0.01596087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006266836540648532,"score_gpt":0.2291681416514745,"score_spread":0.222901305110826,"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."}}