{"id":"W4408824736","doi":"10.5194/oos2025-998","title":"Applications of Computer Vision in Underwater Ecology: A Case Study from the Northeast Pacific","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Underwater; Ecology; Oceanography; Geography; Biology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000356014,0.0001758828,0.000250446,0.00006327074,0.00007583742,0.00003388329,0.0007726671,0.0002012678,0.0001041723],"category_scores_gemma":[0.000006590893,0.0001141649,0.00005236274,0.0001887704,0.0002525359,0.00003418824,0.00445391,0.0004285186,0.00005869719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001782579,"about_ca_system_score_gemma":0.0000141891,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03085539,"about_ca_topic_score_gemma":0.01188748,"domain_scores_codex":[0.9985665,0.0001852367,0.000383766,0.0005282664,0.0001749394,0.000161299],"domain_scores_gemma":[0.9984799,0.0002339885,0.0001089413,0.001150885,0.000008241569,0.00001801782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005814945,0.0005409828,0.9856935,0.00001258363,0.00002968542,0.00006983864,0.002068128,0.001418715,0.00001674538,0.00001857595,0.0004274458,0.009698017],"study_design_scores_gemma":[0.0007133682,0.0002515292,0.9510832,0.0001056355,0.00009471625,0.00003334369,0.0211534,0.003431645,0.0005819608,0.02020605,0.001770082,0.0005749984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912731,0.000006606985,0.005477553,0.001072964,0.0001595702,0.001434863,0.00004899112,0.0001218691,0.000404501],"genre_scores_gemma":[0.9935279,0.00000441875,0.005865638,0.00002514267,0.00002616868,0.0003257367,0.00001269211,0.000006236675,0.0002060281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0346102,"threshold_uncertainty_score":0.9755982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02713631522037978,"score_gpt":0.294297384385086,"score_spread":0.2671610691647062,"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."}}