{"id":"W4388067395","doi":"10.1016/j.rsma.2023.103185","title":"Using unmanned aerial vehicles (UAVs) and machine learning techniques for the assessment of Posidonia debris and marine (plastic) litter on coastal ecosystems","year":2023,"lang":"en","type":"article","venue":"Regional Studies in Marine Science","topic":"Microplastics and Plastic Pollution","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"European Commission","keywords":"Posidonia oceanica; Marine debris; Debris; Environmental science; Litter; Abundance (ecology); Marine ecosystem; Mediterranean sea; Ecosystem; Mediterranean climate; Hydrology (agriculture); Oceanography; Remote sensing; Ecology; Geology; 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":[0.0002785656,0.0003215248,0.0002026074,0.001049105,0.0002395379,0.0004590407,0.0001912563,0.0003746104,0.0003445238],"category_scores_gemma":[0.0004909543,0.000131622,0.0003140395,0.0006700471,0.000161063,0.0005750692,0.0002565205,0.000207687,0.00009970359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002610658,"about_ca_system_score_gemma":0.0002586369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004712363,"about_ca_topic_score_gemma":0.009858296,"domain_scores_codex":[0.9998389,0.00003752944,0.000008495038,0.00004956972,0.00004575819,0.00001967024],"domain_scores_gemma":[0.9997086,0.0001178814,0.00006944333,0.00001669527,0.00006523138,0.00002212718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004443928,0.0004244723,0.2437928,0.0003767955,0.0003584083,0.0003621676,0.0003200293,0.117716,0.1458508,0.0005263112,0.0006279608,0.4891999],"study_design_scores_gemma":[0.00002287577,0.0007403921,0.1868878,0.00004252663,0.0001543675,0.0002610466,0.0006213651,0.7744502,0.03456934,0.0008436477,0.001364065,0.00004244203],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678982,0.000435585,0.03012136,0.00005906381,0.00004014602,0.00004075296,0.0001497308,0.0001008407,0.001154433],"genre_scores_gemma":[0.9663134,0.0002392983,0.03284609,0.00002491349,0.00001152255,0.00001708526,0.00008426548,0.000005755926,0.0004576254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004712363,"threshold_uncertainty_score":0.00936991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05145794250311859,"score_gpt":0.3138496293622562,"score_spread":0.2623916868591377,"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."}}