{"id":"W4383998603","doi":"10.1007/s10661-023-11468-3","title":"Assessment of pixel-oriented k-NN machine learning algorithm performance for the interannual remote sensing monitoring of eelgrass beds at the mouth of the Romaine","year":2023,"lang":"en","type":"review","venue":"Environmental Monitoring and Assessment","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Collège de Maisonneuve; Hydro-Québec; EnGlobe (Canada)","funders":"Hydro-Québec","keywords":"Environmental monitoring; Remote sensing; Environmental science; Ecotoxicology; Pixel; Algorithm; Computer science; Artificial intelligence; Ecology; Geology; Environmental engineering; Biology","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":[0.001902023,0.0006850417,0.001171918,0.002501697,0.0001733931,0.0007370042,0.0009577756,0.0007018305,0.001336437],"category_scores_gemma":[0.002162681,0.0002393343,0.0009253999,0.002395072,0.000223312,0.0007661427,0.0003197735,0.0007486602,0.0005989065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007405621,"about_ca_system_score_gemma":0.001030003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002355304,"about_ca_topic_score_gemma":0.003934518,"domain_scores_codex":[0.9995337,0.0001102095,0.00006723919,0.0000756455,0.0001854976,0.00002776181],"domain_scores_gemma":[0.9993104,0.0002984812,0.0001015235,0.0000212456,0.0002461546,0.00002234637],"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":[0.00009405263,0.0000728789,0.0006593941,0.02023667,0.0003638812,0.00006412084,0.00004450068,0.002768564,0.001422548,0.001823983,0.004669733,0.9677796],"study_design_scores_gemma":[0.0002453882,0.002919764,0.02232952,0.02601378,0.005518501,0.002641299,0.0003036478,0.02946698,0.01283332,0.006573584,0.8908648,0.0002894889],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002349155,0.9931194,0.002122839,0.0002340165,0.0001475303,0.00004372478,0.00006382579,0.00002310819,0.001896436],"genre_scores_gemma":[0.01793632,0.9752778,0.005219304,0.0001605877,0.00009275772,0.0000774665,0.0001597729,0.00001174557,0.001064305],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002501697,"threshold_uncertainty_score":0.010059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149662126248878,"score_gpt":0.2988462292434608,"score_spread":0.277349607980972,"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."}}