{"id":"W4403773712","doi":"10.3390/land13111751","title":"Developing Site-Specific Prescription Maps for Sugarcane Weed Control Using High-Spatial-Resolution Images and Light Detection and Ranging (LiDAR)","year":2024,"lang":"en","type":"article","venue":"Land","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Mitacs","keywords":"Ranging; Lidar; Remote sensing; Weed; Environmental science; Weed control; Geography; Cartography; Agronomy; Biology; Geodesy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001328488,0.0001033852,0.0001171646,0.00001691718,0.0002487791,0.0002221667,0.00002799572,0.00007191027,0.00001192306],"category_scores_gemma":[0.0000110895,0.00003997821,0.00002981618,0.0001062228,0.00001809922,0.0001378377,0.00001735511,0.00005573815,0.000001841455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002554246,"about_ca_system_score_gemma":0.000002797168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001146479,"about_ca_topic_score_gemma":0.00250843,"domain_scores_codex":[0.9993781,0.00002911392,0.0001144744,0.0002369956,0.00007385526,0.0001674571],"domain_scores_gemma":[0.9997801,0.00009964712,0.00002845442,0.00001802775,0.00003546557,0.0000383422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004933658,0.000004872948,0.003956229,0.00003256292,0.00001171187,0.000002392958,0.00006839234,0.000005746678,0.9593059,0.0000925318,0.0001994187,0.03627088],"study_design_scores_gemma":[0.00149774,0.0004241787,0.6980798,0.0004065482,0.0001563339,0.00009593477,0.0002533522,0.007305349,0.06050376,0.002014112,0.2285356,0.0007273302],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912649,0.002769121,0.003900586,0.001243816,0.0003079527,0.0003595899,0.00004773717,0.00008118947,0.00002513401],"genre_scores_gemma":[0.9986871,0.00008530253,0.000268685,0.00004634198,0.000725843,0.00002521272,0.00003966922,0.000001349188,0.0001205054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8988022,"threshold_uncertainty_score":0.2142359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439139640160997,"score_gpt":0.2025839986731847,"score_spread":0.1881926022715747,"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."}}