{"id":"W3197256147","doi":"10.1038/s41598-021-96610-2","title":"Weakly supervised underwater fish segmentation using affinity LCFCN","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Police Service","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Annotation; Pattern recognition (psychology); Pixel; Underwater; Geography","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":[0.0005026466,0.001427976,0.001055277,0.0009319005,0.0005077276,0.0006251804,0.00247824,0.001734319,0.002294015],"category_scores_gemma":[0.00146664,0.0006420757,0.000799336,0.0008952125,0.0006603166,0.001153617,0.001104617,0.001203308,0.001106599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001772063,"about_ca_system_score_gemma":0.001722152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04739647,"about_ca_topic_score_gemma":0.0712564,"domain_scores_codex":[0.9995332,0.00003500002,0.00001592071,0.0002101421,0.0001081117,0.00009766591],"domain_scores_gemma":[0.9994435,0.0001258682,0.00006694419,0.00009957481,0.0002178328,0.00004629696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004723411,0.0002115866,0.005738351,0.0001704476,0.0001648152,0.0002684598,0.0001360444,0.4640904,0.02870619,0.001868758,0.01332904,0.4848435],"study_design_scores_gemma":[0.00000879705,0.00002510469,0.0007698296,0.00000783628,0.00001125134,0.00004211424,0.000009962243,0.9950571,0.002445386,0.0008270332,0.0007869203,0.000008649075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1785901,0.001386866,0.7944364,0.0006651968,0.0002535823,0.0002524525,0.001673306,0.01327231,0.009469751],"genre_scores_gemma":[0.7496915,0.0003344934,0.2240466,0.0009489708,0.000110827,0.00020436,0.005343905,0.0006752862,0.01864408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04739647,"threshold_uncertainty_score":0.0942412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05338972691653265,"score_gpt":0.2779017760483843,"score_spread":0.2245120491318516,"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."}}