{"id":"W4323864574","doi":"","title":"Global in-situ imaging of large plankton and particles by the Underwater Vision Profiler in the upper kilometer of all oceans","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Takuvik Joint International Laboratory; Université Laval","funders":"","keywords":"Underwater; Kilometer; Plankton; Remote sensing; Optical imaging; In situ; Geology; Environmental science; Oceanography; Meteorology; Astronomy; Physics; Optics","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":[0.0001625745,0.0003308652,0.0003547255,0.0007210675,0.0003626312,0.0004067481,0.0002702704,0.0004664691,0.001477183],"category_scores_gemma":[0.0002196983,0.0003144038,0.0003012671,0.0006074087,0.0002794226,0.0006474633,0.000637296,0.0003753069,0.0003290021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003498421,"about_ca_system_score_gemma":0.0004753808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0127521,"about_ca_topic_score_gemma":0.03548607,"domain_scores_codex":[0.9998909,0.000006856571,0.00000289733,0.00003878404,0.00002998474,0.00003053431],"domain_scores_gemma":[0.9998801,0.00001889125,0.00001912557,0.00001240207,0.00004114573,0.00002843354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000531426,0.0001470259,0.1187363,0.0001415055,0.000103106,0.0002616942,0.0005574403,0.004540907,0.8327208,0.0002452615,0.001925577,0.04008901],"study_design_scores_gemma":[0.00004216295,0.0001455953,0.9338146,0.0000172885,0.00007804664,0.0002607474,0.0003316205,0.01219264,0.05022421,0.0001990563,0.002659178,0.000034811],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875187,0.0002202041,0.006094174,0.0001655133,0.00001982298,0.00002506892,0.001307085,0.0002923115,0.004357122],"genre_scores_gemma":[0.9885881,0.0001358952,0.008113134,0.00007332926,0.00001841578,0.00001671162,0.0009131576,0.00008499223,0.002056313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0127521,"threshold_uncertainty_score":0.02535576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759496068756752,"score_gpt":0.2614572125853061,"score_spread":0.2438622518977386,"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."}}