{"id":"W3027468975","doi":"10.3390/s20102991","title":"Quality Control and Pre-Analysis Treatment of the Environmental Datasets Collected by an Internet Operated Deep-Sea Crawler during Its Entire 7-Year Long Deployment (2009–2016)","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ocean Networks Canada Society; University of Victoria","funders":"Helmholtz-Gemeinschaft; Ministerio de Ciencia, Innovación y Universidades","keywords":"Web crawler; Software deployment; Turbidity; Sea trial; Environmental science; Computer science; The Internet; Quality (philosophy); Remote sensing; Engineering; Marine engineering; Geography; Geology; World Wide Web; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00009601509,0.0001843894,0.0002643002,0.00003635939,0.0001336737,0.00005979417,0.000193882,0.00004205608,0.0007900079],"category_scores_gemma":[0.00001337794,0.0001285178,0.00007181022,0.0001816918,0.00009081933,0.0001456259,0.0000437317,0.00005909187,0.00004798703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002678229,"about_ca_system_score_gemma":0.000005535471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001752564,"about_ca_topic_score_gemma":0.0007321594,"domain_scores_codex":[0.9985091,0.0002631122,0.0002863154,0.0004219824,0.0002945185,0.0002249791],"domain_scores_gemma":[0.9993416,0.00004279886,0.0001117519,0.0003106503,0.000003335417,0.0001898519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001931764,0.0001272127,0.9900403,0.00001519204,0.0005490164,0.000009378336,0.0004391644,0.0059197,0.001255369,2.798577e-7,0.0001640375,0.001287191],"study_design_scores_gemma":[0.001056783,0.0002484071,0.9731379,0.000005069535,0.0003218743,0.000001123422,0.0002643293,0.01763496,0.006453608,2.391652e-7,0.0007248805,0.0001508718],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935319,0.0004443141,0.00002113051,0.0001287317,0.00006024126,0.0003697448,0.005378757,0.00001953617,0.00004563813],"genre_scores_gemma":[0.9974169,0.0001896132,0.00003858991,0.00008066374,0.00002654694,0.000003064362,0.001794406,0.000005836451,0.0004443991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01690242,"threshold_uncertainty_score":0.8650032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289653460776431,"score_gpt":0.2185611110107723,"score_spread":0.205664576403008,"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."}}