{"id":"W2304147917","doi":"10.4043/26505-ms","title":"Deep Sea Laser Raman – Past, Present and Future Developments for In Situ Chemical Analysis and Applications","year":2016,"lang":"en","type":"article","venue":"Offshore Technology Conference Asia","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Ocean Sciences","keywords":"Subsea; Methane; Raman spectroscopy; In situ; Deep sea; Environmental science; Computer science; Materials science; Nanotechnology; Process engineering; Geology; Oceanography; Chemistry; Engineering; Optics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006250594,0.0001491787,0.0001926866,0.00003611995,0.00006119638,0.00001134678,0.0001966613,0.0002141903,0.0001049479],"category_scores_gemma":[0.000003616168,0.0001129717,0.00002536173,0.0003473255,0.0005342117,0.00008503183,0.0002726982,0.00008874475,0.00001306596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009712602,"about_ca_system_score_gemma":0.000006251832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000109318,"about_ca_topic_score_gemma":0.0001330111,"domain_scores_codex":[0.999044,0.000009201067,0.0001631384,0.0004271927,0.00009715636,0.0002593232],"domain_scores_gemma":[0.9996205,0.00002403995,0.00005398716,0.0002231607,0.000004796378,0.00007349329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001149885,0.00004566052,0.6633935,0.000003537325,0.00004349471,0.000001744862,0.00004677189,0.00001603195,0.004417685,0.0006134295,0.00005299587,0.3313536],"study_design_scores_gemma":[0.001090771,0.00006983551,0.9436457,0.00001198872,0.0001656691,0.00001623427,0.0008143682,0.002712,0.006232387,0.0080462,0.03669879,0.0004960718],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8910225,0.00004735764,0.101598,0.005610103,0.00001239457,0.0004947945,0.000004806398,0.00006441931,0.001145646],"genre_scores_gemma":[0.9761936,0.0001262944,0.02313777,0.00004133401,0.00003049179,0.0002621429,0.0000101929,0.00000937495,0.0001887663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3308576,"threshold_uncertainty_score":0.4606853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004877678321317165,"score_gpt":0.2084939807319791,"score_spread":0.2036163024106619,"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."}}