{"id":"W7111313197","doi":"10.48321/d131a9f1ae","title":"Tracking climate change as a stress multiplier within vulnerable regions of British Columbia’s inner coastal ocean using a multi-platform approach","year":2025,"lang":"en","type":"other","venue":"California Digital Library","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Baseline (sea); Ocean observations; Climate change; Sampling (signal processing); Ocean current; Tracking (education); Ecosystem; Marine ecosystem","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000201964,0.0008672185,0.001548864,0.0007012577,0.0002634944,0.00231587,0.0009473184,0.001039697,0.0005906902],"category_scores_gemma":[0.0001715723,0.001312913,0.0005261403,0.001223846,0.0006323537,0.002655942,0.001173026,0.001182371,0.0007234879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001085602,"about_ca_system_score_gemma":0.0005886583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006467308,"about_ca_topic_score_gemma":0.003267314,"domain_scores_codex":[0.9950979,0.0001215869,0.001267415,0.001448659,0.0007559448,0.001308523],"domain_scores_gemma":[0.9969851,0.0001156096,0.001280776,0.001030083,0.0001035369,0.0004848641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003492488,0.003997167,0.07840519,0.005740535,0.001228547,0.0009482937,0.0004395069,0.0001850591,0.00005386008,0.000297559,0.9000456,0.00830939],"study_design_scores_gemma":[0.01298338,0.0003170702,0.001252652,0.03781964,0.001169504,0.001267038,0.004390827,0.03147061,0.0002776622,0.00123457,0.8993394,0.008477662],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.008188782,0.001142238,0.0002582001,0.00001223417,0.0003106524,0.002392789,0.5786455,0.001797059,0.4072525],"genre_scores_gemma":[0.1292009,0.0003046496,0.01960905,0.0007271216,0.001539296,0.0003688317,0.07919938,0.01035596,0.7586948],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4994461,"threshold_uncertainty_score":0.9989321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03506819038683828,"score_gpt":0.2377928729642642,"score_spread":0.2027246825774259,"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."}}