{"id":"W3171394146","doi":"10.1016/j.envres.2021.111454","title":"A data-driven binary-classification framework for oil fingerprinting analysis","year":2021,"lang":"en","type":"article","venue":"Environmental Research","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Pancreatic Cancer Action; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Overfitting; Random forest; Classifier (UML); Principal component analysis; Computer science; Artificial intelligence; Binary classification; Support vector machine; Oil spill; Pattern recognition (psychology); Environmental science; Artificial neural network; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002567922,0.0008867398,0.001456432,0.001792642,0.0008176102,0.002127228,0.0035137,0.001543543,0.0031438],"category_scores_gemma":[0.005753126,0.000515603,0.001710423,0.001849788,0.0006337753,0.001755522,0.002145993,0.001968652,0.001693147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143779,"about_ca_system_score_gemma":0.002603593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01536613,"about_ca_topic_score_gemma":0.02058343,"domain_scores_codex":[0.9986396,0.0002452985,0.000110885,0.0003056976,0.000551798,0.0001467305],"domain_scores_gemma":[0.9976088,0.0008433377,0.0001607916,0.0003474075,0.0008972811,0.0001423592],"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":[0.0004861733,0.0007276663,0.005967919,0.0002807344,0.000230932,0.0002467204,0.0001369374,0.3290648,0.01565193,0.03379393,0.0116664,0.6017458],"study_design_scores_gemma":[0.000007127854,0.00001811639,0.0002019601,0.00000762358,0.000009944644,0.00002254234,0.000008538453,0.9880992,0.001434599,0.009058395,0.001123642,0.000008284517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00310271,0.0001150867,0.9937149,0.0001165483,0.00002949252,0.00005274137,0.0003448804,0.002274612,0.0002491579],"genre_scores_gemma":[0.1624349,0.0002094286,0.8315575,0.0002669422,0.000105947,0.0002894711,0.002494075,0.0003443956,0.002297299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01536613,"threshold_uncertainty_score":0.0305534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1095088628269918,"score_gpt":0.378502824262011,"score_spread":0.2689939614350192,"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."}}