{"id":"W4213091245","doi":"10.23919/oceans44145.2021.9705953","title":"Hyperspectral Imaging System for Marine Litter Detection","year":2021,"lang":"en","type":"article","venue":"OCEANS 2021: San Diego – Porto","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Air Canada","funders":"Fundação para a Ciência e a Tecnologia; European Space Agency","keywords":"Hyperspectral imaging; Marine debris; Remote sensing; Litter; Environmental science; Convolutional neural network; Computer science; Artificial intelligence; Oceanography; Geology; Ecology; Biology","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":[0.0002668129,0.0003979293,0.000218603,0.0007908428,0.0002991385,0.000424175,0.0004766794,0.0004862579,0.009235268],"category_scores_gemma":[0.0002173597,0.0001856349,0.0002739749,0.0004794325,0.000147008,0.0005197144,0.0005370974,0.0004701013,0.003445853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003834988,"about_ca_system_score_gemma":0.0004069079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002539661,"about_ca_topic_score_gemma":0.005594175,"domain_scores_codex":[0.9997452,0.00002360315,0.000006933923,0.00005846518,0.0001416879,0.00002407936],"domain_scores_gemma":[0.9998522,0.00001584493,0.00001613841,0.00002656347,0.00007672911,0.00001257429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003078847,0.0002287674,0.006338376,0.0002358453,0.00008219475,0.0001749011,0.0001595283,0.005109498,0.6391002,0.002532268,0.01917164,0.3265589],"study_design_scores_gemma":[0.00007460734,0.000386344,0.04805222,0.0001076961,0.0001103634,0.0009956592,0.0002146933,0.2975262,0.524544,0.002052285,0.1257977,0.0001383863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2418609,0.001065665,0.6526281,0.0007306441,0.0004260805,0.0005935745,0.00639162,0.01878137,0.07752217],"genre_scores_gemma":[0.5196383,0.000767896,0.428051,0.0008105279,0.0001395614,0.0005029299,0.007233939,0.0005477234,0.04230818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009235268,"threshold_uncertainty_score":0.03089505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005022788837958435,"score_gpt":0.1898848261094287,"score_spread":0.1848620372714703,"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."}}