{"id":"W4318184572","doi":"10.5539/ep.v12n1p1","title":"Estimation of Water Hyacinth Using Computer Vision","year":2023,"lang":"en","type":"article","venue":"Environment and Pollution","topic":"Biological Control of Invasive Species","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyacinth; Computer science; Environmental science; Renewable energy; Biomass (ecology); Agricultural engineering; Data mining; Ecology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002984993,0.0004945139,0.0004338537,0.001703623,0.0002232266,0.0007459085,0.0005034183,0.0007152464,0.000942744],"category_scores_gemma":[0.0005550171,0.0001912035,0.0005135043,0.0007249076,0.0002236819,0.0007114012,0.0003853984,0.000370897,0.0005510411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003422911,"about_ca_system_score_gemma":0.0003503377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003246567,"about_ca_topic_score_gemma":0.00311386,"domain_scores_codex":[0.9996802,0.00002986772,0.00001709067,0.0001109788,0.000124277,0.000037618],"domain_scores_gemma":[0.9998005,0.00004337957,0.00003605324,0.00001932446,0.00009120787,0.000009647504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001387176,0.0001542977,0.0092783,0.0002141281,0.00008518583,0.0001635865,0.0001132943,0.02943355,0.1884259,0.001285838,0.001613918,0.7690933],"study_design_scores_gemma":[0.0000149947,0.0002891917,0.0289995,0.00005246069,0.00007331461,0.0005828794,0.0001711921,0.8843027,0.07930622,0.001872196,0.004280617,0.00005471575],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1179945,0.0006879585,0.8757806,0.0001109625,0.00006884268,0.0001009144,0.0001923333,0.001576213,0.003487578],"genre_scores_gemma":[0.6435142,0.0008745333,0.3512564,0.000113776,0.00004021382,0.0001133923,0.0004639434,0.00005002643,0.003573571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003246567,"threshold_uncertainty_score":0.006455362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162263312798187,"score_gpt":0.2075824609731135,"score_spread":0.1859598278451316,"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."}}